Study describes new method to probe the bewildering diversity of the microbiome

In recent years, researchers have begun to explore the vast assemblage of microbes on and within the human body. These include protists, archaea, fungi, viruses, and a large number of bacteria living in symbiotic ecosystems.

Known collectively as the human microbiome, these tiny entities influence an amazing range of activities, from metabolism to behavior, and play a central role in health and disease. Some 39 trillion non-human microbes thrive on and within us, in ceaseless and interdependent agitation. Together they make up more than half of the cells in the human body, although they may have 500 times more genes than are found in human cells. Identifying and making sense of this microbial mixture has been a central challenge for researchers.

In a new study, Qiyun Zhu and colleagues describe a new method to probe the microbiome in unprecedented detail. The technique offers greater simplicity and ease of use compared to existing approaches. Using the new technique, the researchers demonstrate an improved ability to identify biologically relevant characteristics, including a subject’s age and sex based on microbiome samples.

This innovative research promises to rapidly advance investigations into the mysteries of the microbiome. With such knowledge, researchers hope to better understand how these microbes act collectively to protect human health and how their malfunction can lead to a wide range of diseases. Over time, medications and other therapies can even be tailored to the patient’s microbiome profile.

Professor Zhu is a research fellow at 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, Zhu’s former mentor.

The group’s research results appear in the current issue of the journal mSystems.

Tools of the trade

Two powerful technologies have been used to help researchers reveal 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 this study builds on the strengths of both methods to create a new way of processing microbiome data.

“We are borrowing some of the wisdom that has developed from 16S RNA sequencing and applying it to metagenomics,” Zhu said. Unlike other sequencing methods, including 16S, metagenomics allows researchers to sequence all of the DNA information present in a microbiome sample. But the new study shows that the metagenomic approach can still be improved. “The way people currently analyze metagenomic data is limited because whole genome data must first be translated into taxonomy.”

The new technique, known as operational genomic units (OGUs), eliminates 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 basic units for statistical analysis and simply attempts to align sequences present in a sample with sequences found in existing genomic databases.

By doing so, researchers can achieve much finer resolution, which is especially useful when microbes are closely related in the 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, but the OGU approach can help researchers tell them apart.

Furthermore, the method overcomes errors in taxonomy that persist as relics from the pre-sequencing era, when different species were defined by their morphology rather than from DNA sequence data.

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

Innovations in sequencing

The most widely used technique for probing the microbiome, known as 16S ribosomal RNA sequencing or simply 16S, is based on a simple idea. All bacteria have a 16S gene, which is essential for the machinery bacteria need to initiate protein synthesis. The bacterial 16S gene, measuring 1500 base pairs in length, is made up of distinct regions. Some of these regions change very little between different bacteria and over evolutionary periods, while others are highly variable.

The researchers realized that the conserved and variable regions of the 16S gene allow it to act as a molecular clock, keeping track of which bacteria are closer or farther away, based on their sequence similarity. Thus, the 8 conserved regions and the 9 variable regions of 16S can be used to identify bacteria.

To do this, a microbiome sample is first taken. This could be a fecal sample, to assess the gut microbiome, or a skin or mouth sample. Each body site harbors a different bacterial menagerie.

Next, PCR technology is used to amplify portions of the 16S gene. By sequencing highly conserved regions, a wide range of bacteria can be identified, while sequencing variable regions helps narrow down the identity of particular bacteria.

Although 16S is an inexpensive and well-developed method, it has limitations. The technique can only give a general idea of ​​the types of bacteria present, with limited resolution. In general, 16S is only accurate at the level of genus identification.

Enter metagenomic sequencing. This technique sequences the complete genomes of all microbes present in a microbiome sample (not just bacteria, as with 16S). Metagenomics allows researchers to sequence thousands of organisms in parallel, providing precise resolution at the species level. The higher resolution comes at a cost, however. Metagenomic data is much richer and more difficult to analyze than 16S data and more costly in time and money to process.

A new path for metagenomics

The OGU technique streamlines metagenomic sequencing, while providing even greater resolution. The approach classifies microbes in a sample strictly based on their alignment with a reference database – no taxonomic assignment is required. The approach allows researchers to assess the degree of species diversity present in a sample.

Compared to 16S and standard metagenomic sequencing, the new approach is superior in unearthing biologically relevant information. Using the classic Human Microbiome Project dataset of 210 metagenomes sampled from seven body sites of male and female human subjects, the study demonstrates a better correlation between body site and host sex.

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

Further research using even larger datasets will further improve the resolution of the new technique and expand the descriptive power of taxonomy-independent analysis.