• Scientists use machine learning to ident

    From ScienceDaily@1:317/3 to All on Thu Apr 21 22:30:48 2022
    Scientists use machine learning to identify antibiotic resistant
    bacteria that can spread between animals, humans and the environment

    Date:
    April 21, 2022
    Source:
    University of Nottingham
    Summary:
    Experts have developed a ground-breaking software, which combines
    DNA sequencing and machine learning to help them find where, and
    to what extent, antibiotic resistant bacteria is being transmitted
    between humans, animals and the environment.



    FULL STORY ========================================================================== Experts from the University of Nottingham have developed a ground-breaking software, which combines DNA sequencing and machine learning to help
    them find where, and to what extent, antibiotic resistant bacteria is
    being transmitted between humans, animals and the environment.


    ==========================================================================
    The study, which is published in PLOS Computational Biology, was led by
    Dr Tania Dottorini from the School of Veterinary Medicine and Science
    at the University.

    Anthropogenic environments (spaces created by humans), such as areas
    of intensive livestock farming, are seen as ideal breeding grounds for antimicrobial-resistant bacteria and antimicrobial resistant genes,
    which are capable of infecting humans and carrying resistance to drugs
    used in human medicine. This can have huge implications for how certain illnesses and infections can be treated effectively.

    China has a large intensive livestock farming industry, poultry being
    the second most important source of meat in the country, and is the
    largest user of antibiotics for food production in the world.

    In this new study, a team of experts looked at a large scale commercial
    poultry farm in China, and collected 154 samples from animals, carcasses, workers and their households and environments. From the samples, they
    isolated a specific bacteria called Escherichia coli (E. coli).These
    bacteria can live quite harmlessly in a person's gut, but can also be pathogenic, and genome carry resistance genes against certain drugs,
    which can result in illness including severe stomach cramps, diarrhea
    and vomiting.

    Researchers used a computational approach that integrates machine
    learning, whole genome sequencing, gene sharing networks and mobile
    genetic elements, to characterise the different types of pathogens found
    in the farm. They found that antimicrobial genes (genes conferring
    resistance to the antibiotics) were present in both pathogenic and non-pathogenic bacteria.

    The new approach, using machine learning, enabled the team to uncover an
    entire network of genes associated with antimicrobial resistance, shared
    across animals, farm workers and the environment around them. Notably,
    this network included genes known to cause antibiotic resistance as well
    as yet unknown genes associated to antibiotic resistance.

    Dr Dottorini said: "We cannot say at this stage where the bacteria
    originated from, we can only say we found it and it has been shared
    between animals and humans. As we already know there has been sharing,
    this is worrying, because people can acquire resistances to drugs from two different ways -- from direct contact with an animal, or indirectly by
    eating contaminated meat. This could be a particular problem in poultry farming, as it is the most widely used meat in the world.

    "The computational tools that we have developed will enable us to
    analyse large complex data from different sources, at the same time as identifying where hotspots for certain bacteria may be. They are fast,
    they are precise and they can be applied on large environments -- for
    instance -- multiple farms at the same time.

    "There are many antimicrobial resistant genes we already know about, but
    how do we go beyond these and unravel new targets to design new drugs?
    "Our approach, using machine learning, opens up new possibilities for
    the development of fast, affordable and effective computational methods
    that can provide new insights into the epidemiology of antimicrobial
    resistance in livestock farming." The research was done in collaboration
    with Professor Junshi Chen, Professor Fengqin Li and Professor Zixin
    Peng from China National Center for Food Safety Risk Assessment (CFSA).


    ========================================================================== Story Source: Materials provided by University_of_Nottingham. Note:
    Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Zixin Peng, Alexandre Maciel-Guerra, Michelle Baker, Xibin Zhang,
    Yue Hu,
    Wei Wang, Jia Rong, Jing Zhang, Ning Xue, Paul Barrow, David Renney,
    Dov Stekel, Paul Williams, Longhai Liu, Junshi Chen, Fengqin Li,
    Tania Dottorini. Whole-genome sequencing and gene sharing network
    analysis powered by machine learning identifies antibiotic
    resistance sharing between animals, humans and environment in
    livestock farming. PLOS Computational Biology, 2022; 18 (3):
    e1010018 DOI: 10.1371/ journal.pcbi.1010018 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220421094046.htm

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