The study presents a new way to look at microbiome diversity

microbe

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In recent years, researchers have begun to investigate the large group of microbes that live in the human body. This includes protists, archaea, fungi, viruses and many bacteria that live in symbiotic ecosystems.

Also known as the human microbiome, these tiny organisms have influenced amazing processes, from metabolism to function and play an important role in health and disease. There are 39 trillion non-human microbes that grow inside and out of us, in an unrelated cluster. Together, they make up more than half of the cells of the human body, although they can contain up to 500 times the number of genes found in human cells. Identifying and understanding this microbial is important for researchers.

In a new study, Qiyun Zhu and his colleagues describe a new way to test the microbiome in unseen details. The technology offers a simplicity and ease of use compared to existing methods. Using the latest technology, the researchers demonstrate the ability to identify biologically related patterns, including the age of the subject and gender relative to microbiome samples.

The new research holds promise for rapid progress in research into the mysteries of the microbiome. With that knowledge, researchers hope to better understand how these microbes play a role in maintaining human health and how their harmful actions can lead to widespread diseases. Over time, medications and other therapies can be developed based on the microbiomic knowledge of the patient.

Professor Zhu is a researcher at the Biodesign Center for Fundamental and Applied Microbiology and ASU’s School of Life Science. The research team consists of colleagues from the University of California, San Diego, with co -author Rob Knight, Zhu’s former professor.

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

The tools of the trade

Two powerful technologies have been used to help researchers unravel the diversity and complexity of the microbiome, by sequencing the microbial DNA into a sample. These include 16S and metagenomic sequencing. The technology described in the current study draws on the forces of both types to create a new way of transferring data from the microbiome.

“We ate some of the knowledge developed from 16S RNA sequencing and applied it to metagenomics,” Zhu said. Unlike other sequencing techniques, such as 16S, metagenomics allows researchers to sequence all the DNA information found in a microbiome sample. But new research shows that the metagenomic pathway has room for improvement. “The way people now look at metagenomic data, is because the whole genome data has to be translated into taxonomy first.”

The new technology, Operational Genomic Units (OGU) is seen to eliminate the labor and sometimes fraudulent nature of assigning tax characteristics such as genus and characteristics to the population of microbes in an environment. an example. Instead, the method uses individual genomes as base segments for statistical analysis and only attempts to compare the sequences found in a sample to the sequences found in the available genomic data.

By doing this, researchers can come up with better solutions, especially if microbes are found that are closely related to the DNA sequence. This is true because most tax breaks are based on similarities. If the two levels differ below a threshold, they fall into the same tax category, but the OGU approach can help researchers tell them.

In addition, the path overcomes errors in a taxonomy that continues as relics from the pre-sequencing epoch, when the different species are explained by their morphology rather than by DNA sequence data.

In addition to improving resolution and simplicity, OGU can help researchers analyze data using what is known to be a phylogenetic drug. As the name implies, these are members that can describe the level of relationship between living things, because of their similarity. Just as two distant species such as worms and antelopes can be found in the most distant branches of a phylogenetic plant, so can distant bacteria and other parts of the microbiome.

Updates to the setup

The most widely used technology for microbiome testing, known as 16S ribosomal RNA sequencing or 16S alone, relies on a simple concept. All bacterial seeds contain the 16S gene, which is important in mechanical bacteria that initiate protein synthesis. The 16S bacteria series, which measures 1500 bases in length, has different segments. Some of these areas vary slightly between different bacteria and over evolutionary periods, while others are highly variable.

The researchers found that the protective parts and fragments of the 16S gene allow it to function as a molecular clock, keeping track of bacterial infections that are more closely related and more distant, related to their similarities. Therefore, 8 stored segments and 9 segments of 16S can be used as fingerprint bacteria.

To do this, a microbiome sample was first collected. This may be a fecal sample, to examine the microbiome of the stomach, or a sample from the skin or mouth. Each immune system is home to a different bacterial menagerie.

Then, PCR technology was used to amplify the fragments of the 16S gene. By sorting out the most protected areas, a wide area of ​​bacteria can be identified, and sorting the different areas helps to reduce the presence of some bacteria.

While the 16S is a simple and well -developed track, there are limitations. The technology is able to provide a general idea of ​​the types of bacteria found, with limited resolution. In general, the 16S is only accurate at the genus level of knowledge.

Enter the metagenomic process. This technology applies to the complete genomes of all microbes found in a single microbiome sample, (not just bacteria, e.g. 16S). Metagenomics allows researchers to organize thousands of organisms in parallel, providing a precise, level -based solution. The bigger decision comes with costs. Metagenomic data is more valuable and more difficult to calculate than 16S data and costs more time and money to produce.

A new approach to metagenomics

The OGU technology integrates the metagenomic process, while providing a more compact solution. The path separates the microbes in a sample according to their alignment with a reference source – no tax requirements. The researcher can evaluate the level of different variables in a sample.

Compared to 16S and the conventional metagenomic process, the new method is more efficient in capturing biological information. Using standard Human Microbiome Project data of 210 metagenomes compared from seven male and female body sites, the study shows a positive relationship between the body site and the female host.

Then, a total of 6,430 stool samples collected were analyzed through the Finnish population sample, using 16S and metagenomic sequencing. The examples are from a large, randomized group of the Finnish population, called FINRISK. The goal was to predict the age of the target population, relative to the formation of microbial gut. In addition, the OGU is superior to 16S and standard metagenomic analysis, providing more accurate predictions.

Taking new research into larger data will increase the flexibility of the new technology and increase the interpretive power of taxonomy -independent data.


The new sequencing technology is developed for low, degraded or contaminated microbiome samples


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

Presented by Arizona State University

Directions: Study presents a new method for finding microbiome diversity (2022, April 4) retrieved April 4, 2022 from https://phys.org/news/2022-04-method- probing-bewildering-diversity-microbiome.html

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