AsianScientist (Apr. 25, 2022)– There are more than 39 trillion microorganisms in our bodies. They work together to regulate our body functions such as digestion, development, organ growth, aging, and also protect us from various pathogens. Their slight malfunction can result in a variety of disorders. So, researchers for a long time have been trying to identify the composition of these microbiomes in our body.
In a new research published in the journal mSystems, Qiyun Zhu a computational biologist at the Arizona State University and his colleagues came up with a new method of studying microbiome in great detail. They call this system Operational Genomic Units or simply OGU. Using this method of microbiome analysis, they claim to even reliably predict the host’s age and sex.
Categorizing microbiomes is tough because there are simply a lot of them: non-cellular viruses, prokaryotic bacteria and eukaryotes like fungi and protists. The new method took inspiration from two of the conventional processes used in microbial identification: 16S rRNA sequencing (or simply 16S) and metagenomic sequencing.
16S is a relatively old method. In bacterial cells, there are three types of ribosomal RNA—23S, 16S and 5S. In 16S ribosomal RNA, there are about 1500 base pairs. It can be easily sequenced, and contains plenty of historical information, which is why it is often used as a standard in bacterial taxonomic classification.
But sometimes, it may not be always possible to culture the microorganisms in order to identify them. In such cases, researchers can directly obtain genes from environmental samples and sequence total DNA extracted from the microbial community. Such culture-independent analysis of the collective genomes of microorganisms is called metagenomic sequencing.
In the study, the researchers combined both the methods to create a new way of processing data from the microbiome. “We borrowed some of the wisdom from 16S RNA sequencing and applied it to metagenomics,” Zhu says. Metagenomics allows researchers to sequence all the DNA information present in a microbiome sample. “[But] the way people currently analyze metagenomic data is limited, because whole genome data has to be first translated into taxonomy.”
The new analysis technique, Operational Genomic Units (OGU), used by Zhu’s team takes inspiration from a well-known taxonomic process called Operational Taxonomic Units (OTU). OTU is used to classify closely related organisms by grouping them based on their DNA sequence similarity of a specific taxonomic marker gene. But the method is difficult, laborious and sometimes misleading in assigning taxonomic categories like genus and species to the multitude of microbes present in a sample. Instead, in OGU, individual genomes are used as the basic units for statistical analysis. The technique first aligns sequences present in a sample and compares them to the sequences found in existing genomic databases. By doing that, the researchers can get into fine details and distinguish even minimal differences in DNA sequences.
Microbiome research is entering into the “genomic era”, says Sangeet Lamichhaney, a Nepalese evolutionary genomics researcher at Kent State University in Ohio, “[where] we are regularly using large scale metagenomics approaches to examine micro-biome diversity.” But the sheer volume of such heterogeneous datasets provides challenges for their effective integration and analysis. While OGU is an improved method for taxonomic classification of microbiome diversity using genomic data, “it still has limitations in terms of computational algorithms used.” Making such tools “user friendly and error free” is the next challenge lying ahead, lamichhaney says.
Source: Arizona State University; Photo: Shutterstock
The article can be found at Zhu et al (2022) Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy