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


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

Collectively known as the human microbiome, these tiny units influence an amazing range of activities, from metabolism to behavior, and play central roles in health and disease. Some 39 trillion nonhuman microbes thrive on and within us in a ceaseless, interdependent hustle and bustle. Together they make up over half of the human body’s cells, although they contain potentially 500 times as many genes as are found in human cells. Identifying and understanding this microbial mix has been a key challenge for researchers.

In a new study, Qiyun Zhu and his colleagues describe a new way to study 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 localize biologically relevant traits, including a subject’s age and sex, based on microbiome samples.

The innovative research promises rapid progress in exploring the mysteries of the microbiome. With this knowledge, the researchers hope to better understand how these microbes work together to protect human health and how their malfunction can lead to a variety of diseases. Over time, drugs and other therapies can even be tailored based on a patient’s microbiomic profile.

Professor Zhu is a researcher at the Biodesign Center for Fundamental and Applied Microbiology and School of Life Sciences at ASU. The research team includes collaborators from the University of California, San Diego, including co-correspondence author Rob Knight, Zhu’s former mentor.

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

tools of trade

Two powerful technologies have been employed 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 uses the strengths of both methods to create a new way of processing data from the microbiome.

“We’re borrowing some of the wisdom that has evolved from 16S RNA sequencing and applying it to metagenomics,” says Zhu. 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 still has room for improvement. “The way people currently analyze metagenomic data is limited because whole genomic data must first be translated into taxonomy.”

The new technique, known as Operational Genomic Units (OGU), eliminates the tedious and sometimes misleading practice of assigning taxonomic categories, such as 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 match sequences present in a sample with sequences found in existing genomic databases.

This allows researchers to achieve much finer-grain resolution, which is particularly useful when microbes that are closely related in DNA sequence are present. This is true because most taxonomic classifications are based on sequence similarity. When two sequences differ by less than a certain threshold, they fall into the same taxonomic category, but the OGU approach can help researchers distinguish them from one another.

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

In addition to improving resolution and simplicity, OGU can help researchers analyze data using so-called phylogenetic trees. As the name suggests, these are branched structures that can describe the degree of relatedness between organisms based on their sequence similarity. Just as two more distantly related species such as worms and antelope appear on more distant branches of a phylogenetic tree, more distantly related bacteria and other components of the microbiome will also appear.

Innovations in sequencing

The most widely used technique for probing the microbiome, known as 16S ribosome RNA sequencing, or simply 16S, stems from a simple idea. All bacteria have a 16S gene, which is essential for the machinery bacteria need to initiate protein synthesis. The 1500 base pair bacterial 16S gene consists of different regions. Some of these regions change very little between different bacteria and over evolutionary timescales, 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, tracking bacteria that are more closely or more distantly related based on their sequence similarity. Thus, the 8 conserved and 9 variable regions of 16S can be used to fingerprint bacteria.

First, a microbiome sample is taken. This can be a fecal sample to assess the gut microbiome, or a skin or mouth sample. Each part of the body houses a different menagerie of bacteria.

Next, PCR technology is used to amplify parts of the 16S gene. Highly conserved region sequencing allows a wide range of bacteria to be identified, while variable region sequencing helps narrow the identity of specific bacteria.

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

Enter metagenomic sequencing. This technique sequences the entire genomes of all microbes present in a microbiome sample (not just bacteria, as in 16S). Metagenomics allows researchers to sequence thousands of organisms in parallel, allowing for accurate species-level resolution. However, the higher resolution comes at a cost. Metagenomic data is far richer and more computationally challenging to analyze than 16S data, and more time and cost intensive to process.

A new avenue for metagenomics

The OGU technique streamlines metagenomic sequencing while providing even higher resolution. The approach strictly classifies microbes in a sample according to their comparison to a reference database – no taxonomic mapping is required. The approach allows researchers to assess the level of biodiversity in a sample.

Compared to 16S and standard metagenomic sequencing, the new approach is superior in detecting biologically relevant information. Using the classic Human Microbiome Project data set of 210 metagenomes taken from seven body sites of male and female subjects, the study shows 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, randomly selected cohort of the Finnish population known as FINRISK. The aim was to predict the age of the sampled individuals based on the microbial composition of the gut. Again, the OGU method outperformed 16S and traditional metagenomic analysis, providing more accurate predictions.

New research results, based on even larger datasets, will further improve the resolution of the new technique and expand the descriptive power of the taxonomy-independent analysis.

New sequencing technique established for microbiome poor, degraded or contaminated microbiome samples

More information:
Qiyun Zhu et al, Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes under Bypassing Taxonomy, mSystems (2022). DOI: 10.1128/msystems.00167-22

Provided by Arizona State University

citation: Study describes new method for probing the bewildering diversity of the microbiome (2022, April 4), retrieved on April 4, 2022 by

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