• New method for probing the bewildering d

    From ScienceDaily@1:317/3 to All on Mon Apr 4 22:30:44 2022
    New method for probing the bewildering diversity of the microbiome


    Date:
    April 4, 2022
    Source:
    Arizona State University
    Summary:
    Scientists describe a new method for probing the microbiome in
    unprecedented detail. The technique provides greater simplicity
    and ease of use compared with existing approaches. Using the
    new technique, the researchers demonstrate an improved ability
    to pinpoint biologically relevant characteristics, including a
    subject's age and sex based on microbiome samples.



    FULL STORY ==========================================================================
    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 vast numbers of bacteria living in symbiotic ecosystems.


    ========================================================================== Known collectively as the human microbiome, these tiny entities influence
    an astonishing range of activities, from metabolism to behavior and
    play a central role in health and disease. Some 39 trillion non-human
    microbes flourish on and within us, in a ceaseless, interdependent
    bustle. Together, they make up over half of the human body's cells,
    though they may possess 500 times as many genes as are found in human
    cells. Identifying and making sense of this microbial me'lange has been
    a central challenge for researchers.

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

    The innovative research holds the promise of rapidly advancing
    investigations into the mysteries of the microbiome. With such knowledge, 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.

    Professor Zhu is a researcher in the Biodesign Center for Fundamental
    and Applied Microbiology and ASU's School of Life Sciences. 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 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
    draws on the strengths of both methods to create a new way of processing
    data from the microbiome.

    "We borrow some of the wisdom that developed from 16S RNA sequencing and
    apply it to metagenomics," Zhu says. Unlike other sequencing methods,
    including 16S, metagenomics allows researchers to sequence all the DNA information present in a microbiome sample. But the new study shows
    that the metagenomic approach has room for improvement. "The way people currently analyze metagenomic data is limited, because whole genome
    data has to first be translated into taxonomy." The new technique,
    known as Operational Genomic Units (OGU) 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.



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

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

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

    Researchers realized that the conserved and variable regions of the 16S
    gene allow it to act as a molecular clock, keeping track of 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.

    To do this, a microbiome sample is first collected. This could be a fecal sample, to evaluate the gut microbiome, or a sample from the skin or
    from the mouth. Each body site is home to a different bacterial menagerie.

    Next, PCR technology is used to amplify portions of the 16S gene. By
    sequencing highly conserved regions, a broad swath of bacteria can
    be identified, while sequencing of variable regions helps narrow 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 kinds of bacteria
    present, with limited resolution. In general, 16S is only accurate to
    the genus level of identification.

    Enter metagenomic sequencing. This technique sequences the full 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 accurate, species-level resolution. The greater
    resolution however does come with costs. Metagenomic data is far richer
    and more computationally challenging to analyze than 16S data and more expensive 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 according to their alignment with
    a reference database -- no taxonomic assignment required. The approach
    enables researchers to evaluate the degree of species diversity present
    in a sample.

    Compared with 16S and standard metagenomic sequencing, the new approach
    is superior in ferreting out 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 better correlation between body site and host sex.

    Next, 6,430 stool samples collected through a random sampling of
    the Finnish population were analyzed, using both 16S and metagenomic sequencing. The samples belong to a large, randomly sampled cohort of
    the Finnish population, known as FINRISK. 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.


    ========================================================================== Story Source: Materials provided by Arizona_State_University. Original
    written by Richard Harth. Note: Content may be edited for style and
    length.


    ========================================================================== Journal Reference:
    1. Qiyun Zhu, Shi Huang, Antonio Gonzalez, Imran McGrath, Daniel
    McDonald,
    Niina Haiminen, George Armstrong, Yoshiki Va'zquez-Baeza, Julian
    Yu, Justin Kuczynski, Gregory D. Sepich-Poore, Austin D. Swafford,
    Promi Das, Justin P. Shaffer, Franck Lejzerowicz, Pedro Belda-Ferre,
    Aki S.

    Havulinna, Guillaume Me'ric, Teemu Niiranen, Leo Lahti, Veikko
    Salomaa, Ho-Cheol Kim, Mohit Jain, Michael Inouye, Jack A. Gilbert,
    Rob Knight.

    Phylogeny-Aware Analysis of Metagenome Community Ecology Based
    on Matched Reference Genomes while Bypassing Taxonomy. mSystems,
    2022; DOI: 10.1128/ msystems.00167-22 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220404185543.htm

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