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    Home > Biochemistry News > Biotechnology News > Nature sub-issue: Just turn off this backup gene and the cancer will be alleviated

    Nature sub-issue: Just turn off this backup gene and the cancer will be alleviated

    • Last Update: 2022-10-01
    • Source: Internet
    • Author: User
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    Researchers at the University of Michigan and Indiana University have shown that the way tumor cells grow uncontrollably is also a weakness that can be harnessed to treat cancer
    .


    The team demonstrated this new precision medicine approach
    to treating ovarian cancer in mice.


    Deepak Nagrath, associate professor of biomedical engineering at the University of Michigan and senior author of the study in Nature Metabolism, said: "This could revolutionize the field of precision medicine, as drug targeting only affects and kills cancer cells, not normal cells
    .


    This approach is known as incidental lethality — using information gathered from genes discarded by cancer cells to look for weaknesses
    .


    In doing so, cells often lose other genes
    necessary for survival.


    What if you could identify the right metaphor and lock it in in a way that shuts down important functions of the cell?

    "When deleted metabolic genes cannot be directly replaced, our algorithm uses mathematical models of cancer cell metabolism to predict the parahomologous metabolic pathways they may use," said
    Abhinav Achreja, a biomedical engineering researcher at the University of Michigan and first author of the study paper.


    Attacking metabolic pathways is essentially shutting down the cell's energy source
    .


    Interference with this process can lead to a serious imbalance
    of NAD+, an important metabolite in the mitochondria where respiration occurs.


    U-M's algorithm correctly sorted multiple options and successfully predicted that a cell deficient of UQCR11 would turn to the gene MTHFD2 as a backup provider
    for its NAD+.


     Metabolic collateral lethal target identification reveals MTHFD2 paralog dependency in ovarian cancer

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