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    Home > Biochemistry News > Biotechnology News > 【Scientific research dynamics】Professor Xue Yu's team collaborated to propose a cross-omic core kinase inference algorithm

    【Scientific research dynamics】Professor Xue Yu's team collaborated to propose a cross-omic core kinase inference algorithm

    • Last Update: 2022-09-21
    • Source: Internet
    • Author: User
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    On September 6th, the team of Professor Xue Yu of the School of Life Science and Technology of our university cooperated with the team of Huang Pengyu of the Institute of Biomedical Engineering of the Chinese Academy of Medical Sciences and peking Union Medical College, and the team of Shu Yilai of the Eye, Ear, Nose and Throat Hospital affiliated to Fudan University to cooperate with the international journal "Nature? Nature Communications published online a collaborative effort
    titled "PIM1 promotes hepatic conversion by suppressing reprogramming-induced ferroptosis and cell cycle arrest.
    " 。 The co-first authors of the paper are Dr.
    Yangyang Yuan, Dr.
    Chenwei Wang, Xuran Zhuang and Dr.
    Shaofeng Lin, of whom Dr.
    Wang Chenwei and Dr.
    Lin Shaofeng are members of the Xue Yu team, and the co-corresponding authors are Professor Huang Pengyu, Professor Xue Yu and Professor
    Shu Yilai.

    Protein kinase-mediated phosphorylation is an important chemical modification that dynamically regulates subcellular localization, conformation, homeostasis, interaction, association, and activity of functional proteins, reversibly determining cell dynamics and plasticity
    .
    However, the activity of protein kinase is regulated by multiple levels such as transcription level, protein level, protein phosphorylation level, etc.
    , and it is difficult to systematically assess
    the activity of protein kinase through a single omics data.
    In this work, the researchers integrated phosphorylation group and transcriptome data to develop a cross-omics "central kinase inference" (CKI) algorithm
    .

    The design of the CKI algorithm consists of three basic assumptions
    .
    First, the expression of protein kinases at the transcriptional level regulates their inherent activity, so protein kinases with significant changes in transcriptional levels may be important
    in biological processes.
    Differentially expressed protein kinases can be calculated from transcriptome data
    .
    Second, more active kinases may modify more phosphorylation sites and vice versa
    .
    Therefore, the changes in the activity of upstream regulatory kinases can be inferred according to the modification site changes
    in the phosphorylation group data.
    Third, higher activity kinases may have a stronger effect on phosphorylation networks, so kinase-substrate networks can be constructed from phosphorylation group data, and then statistical methods are used to detect kinases
    with significantly changed activity.
    The three-part prediction is the final result
    of the CKI algorithm.

    Using the CKI algorithm, the key role
    of the protein kinase PIM1 in the early stage of transdifferentiation from human skin fibroblasts to hepatocytes was predicted and discovered.
    Subsequent experiments have found that PIM1 greatly improves the efficiency of hepatic transdifferentiation by inhibiting cell cycle block and cell iron death caused by cell fate transition, and can significantly promote the maturation
    of transdifferentiated hepatocytes.
    Using the CKI algorithm, this work also identified the mechanism by which mouse hepatic progenitor cells with bidifferentiation potential promote hepatocyte maturation through kinases such as PRKACA, PRKACB, and PRKX during differentiation into mature hepatocytes
    .
    Therefore, this work develops a new cross-omics analysis method CKI, which provides a new tool
    for the discovery of core protein kinases in important biological processes.


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