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    Home > Biochemistry News > Biotechnology News > Primate-specific genes are involved in the cell cycle processes of the brain in cancer and embryonic stages

    Primate-specific genes are involved in the cell cycle processes of the brain in cancer and embryonic stages

    • Last Update: 2022-12-30
    • Source: Internet
    • Author: User
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    Dubzansky's famous quote "Nothing in biology makes sense except in the light of evolution" summarizes the significance of evolution for various fields of life sciences [1].

    Taking cancer biology as an example, the ideas and methods of evolutionary biology have been widely used [2, 3]; The atavism hypothesis and its related research is a new direction in this field in recent years [4, 5].

    This hypothesis suggests that cancer is the reversal of multicellular life into the evolution of its single-celled ancestor, which is manifested by the upregulation of the expression of unicellular genes (UC) during the single-cell ancestor period and the downregulation of the early metazoan (EM) gene in the early metazoan evolution period to drive cancer development [6, 7].

    。 Another hypothesis that has been explored relatively little is antagonistic pleiotropy, where evolutionary adaptive selection of recently altered genes maximizes fit in young or young individuals; At this cost, these mutations may trigger diseases of old age, including cancer [8, 9].

    If both hypotheses are true, it can be predicted that newly produced primate-specific genes (PSGs) in the human genome fixed by adaptive selection should exhibit cancer-promoting functions overall, especially primate-specific repetitive genes derived from UC genes
    .
    Testing of this joint prediction is not only useful for understanding cancer susceptibility and identifying oncogenes; It also helps to understand how PSGs drive the evolution of adaptive phenotypes
    .

    The Daniel Zhang research group and collaborators of the Institute of Zoology, Chinese Academy of Sciences explored the above questions by integrating data from The Cancer Genome Atlas (TCGA), brain development transcriptome [10] and primate-specific gene collection [11], and the relevant results were published in Genome Biology (GB) on December 6, 2022, with the paper "Pan-cancer surveys indicate cell.
    " cycle-related roles of primate-specific genes in tumors and embryonic cerebrum" (https://doi.
    org/10.
    1186/s13059-022-02821-9).

    In order to obtain a more comprehensive genetic age data, the researchers integrated the results based on two gene age inference strategies (Fig.
    1A): a protein family-based strategy (suitable for inferring UC and EM gene age) and a genome colinearity-based strategy (suitable for inferring the age of PSGs genes).

    The proportion of tissue-specific expression genes in PSGs collection increased significantly.
    Consistent with the atavistic hypothesis, cross-cancer transcriptome analysis showed that UC and EM were up-regulated and down-regulated
    in cancer, respectively.
    PSGs also tend to rise in expression in cancer samples, a result consistent with the prediction of the antagonistic pleiotropy hypothesis (Fig.
    1B
    ).
    It is worth noting that the upregulation of PSGs is not contributed by tissue-specific expression genes alone, and broad-spectrum expression of PSGs is also elevated in cancer (Fig.
    1C).

    By identifying cross-cancer up-regulated genes and cross-cancer down-regulated genes, the researchers found that PSGs enriched cross-cancer upregulated genes, UC genes showed a similar trend, while EM genes showed the opposite trend (Fig.
    1D).

    In order to explore the function of up-regulating PSGs, the researchers integrated survival analysis, pathway enrichment analysis and gene necessity analysis of cancer cell lines
    .
    Firstly, survival analysis showed that the cross-cancer upregulated genes uPSGs and uUC were highly correlated with adverse clinical prognosis, that is, high expression tended to promote tumors.
    The cross-cancer down-regulation gene dEM is the opposite (Fig.
    2A).

    Biological pathway enrichment analysis suggests that uUC and uPSGs are significantly enriched in cell cycle-related functions (Fig.
    2B), and echoing the atavistic theory, uPSGs involved in the cell cycle are mostly derived copies of UC genes (Fig.
    2C).

    Finally, by correlating with the necessary gene information that significantly affects cell line proliferation, the researchers prioritized 15 uPSGs (Fig.
    2D-E) with cancer-promoting functions, among which DDX11 gene related to cell cycle function showed broad-spectrum cancer-promoting function (Fig.
    2F).

    DDX11 is involved in cell cycle processes
    such as sister chromatid condensation.
    Researchers infer through evolutionary analysis that they have undergone drastic changes
    in primate evolution.
    First, based on the collinearity information, the researchers found that DDX11 now annotated in the human genome is a derived functional copy (Fig.
    3A) generated by gene repeat events, and its paralineal homologous copy has been pseudogenomized (Fig.
    3B).

    The derived copy DDX11 protein exhibits accelerated evolution in specific domains (Fig.
    3C), and its expression is upregulated relative to the outer group (Fig.
    3D), suggesting a functional adaptive change
    .

    According to the antagonistic pleiotropic hypothesis, cell cycle-related PSGs that promote cancer occurrence are involved in biological functions
    under adaptive selection.
    Considering that the expansion of the cerebral cortex of primates (especially humans) is accompanied by a large proliferation of neural progenitor cells, it is speculated that PSGs performing cell cycle functions are involved in these biological processes
    .
    Through the analysis of transcriptome data of the brain development process, the researchers found that PSGs were concentrated in the earliest embryonic brain development period (4-7 weeks after pregnancy), and the cell cycle-related uPSGs including DDX11 were highly expressed in this period
    .
    Consistent with the positive selection signal of DDX11, this stage of embryonic development significantly enriches genes with coding sequences or promoter regions undergoing positive selection (Fig.
    4).

    By integrating functional genomic and evolutionary genomic data, this work found that PSGs that are widely upregulated in tumors tend to perform cell cycle-related functions, and that these genes are mainly derived duplicate copies
    of UC genes.
    The above results are consistent with the joint predictions of the atavistic and antagonistic pleiotropic hypotheses: the high expression of cell cycle-specific genes associated with cell cycles that join genetic networks in recent evolution mediates the expansion of the cerebral cortex; At the cost, these genes also increase the risk of
    cancer.
    In other words, atavism and antagonistic pleiotropy together contribute to cancer susceptibility; The origin of new genes, regulation of old genes, or changes in protein sequences combine to lead to systematic adjustments
    in cell cycle programs during embryonic brain development.

    Daniel Zhang researchers have long been working on the origin of new genes, and have revealed how transposability and tandem repeating processes mediate the origin of new genes (Genome Research 2016, Nature Communications 2021, Nature Ecology & Evolution 2022); The latest work of GB, together with (Genome Research 2019) and (Developmental Cell 2021), forms a series of explorations of how new genes can promote the evolution of human phenotypes
    .
    The study was done
    in collaboration with institutions such as the Institute of Zoology and Beihang University.
    Ma Chenyu, a doctoral student at the Institute of Zoology, Li Chunyan, an associate researcher at the School of Medical Science and Engineering of Beihang University, and Ma Huijing, a postdoctoral fellow at the Institute of Zoology, are co-first authors, and Daniel Zhang researchers are the corresponding authors
    .
    Yu Daqi, Zhang Yufei, Zhang Dan, Su Tianhan, Wu Jianmin, Wang Xiaoyue, Zhang Li, Chen Chunlong and other collaborators provided strong support
    in article writing, experimentation and computational analysis.
    This research has been supported by the National Key Research and Development Program of China (2019YFA0802600), Chinese Academy of Sciences (XDPB17, ZDBS-LY-SM005, XBZG-ZDSYS-201913), National Natural Science Foundation of China (31970565, 91731302), and Beijing Brain Research Center Open Research Project
    .

    Link to paper: https://doi.
    org/10.
    1186/s13059-022-02821-9

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      11.
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    Fig.
    1 Age inference of gene origin and analysis of its expression pattern in cancer
    .
    (a) Age grouping chart
    of genetic origin.
    Red (9-14 branches) are generated based on the collinearity strategy, and the remaining branches are generated
    based on the protein family strategy.
    (B) Expression abundance of 14 age group genes in cancer versus corresponding normal
    tissues.
    (C) Statistical plot
    of the difference between broad-spectrum expression of PSGs and tissue-specific expression of PSGs in cancer samples.
    (D) Comparison
    of the proportion of up- and down-regulated genes across cancer species.

    Fig.
    2 Functional analysis
    of upregulated PSGs (uPSGs) across cancer species.
    (A) Higher expression of uPSGs or uUC genes is often associated with
    shorter survival times compared to dEM genes.
    (B) The proportion of
    genes associated with cell cycle function in different types of gene sets.
    (C) Proportion
    of PSGs inferred to be a UC gene-derived copy.
    (D) Heat map
    of 15 uPSGs required for at least one cancer.
    (E) Distribution of the number of gene-dependent cell lines
    .
    (F) Density distribution of genes with co-dependent scoring across cell lines
    .

    Fig.
    3 DDX11 evolutionary analysis
    .
    (A).
    Collinear plot
    of five primates.
    (B) DDX11 paralineage homologous gene accumulation loss-of-function mutations
    .
    (C) Ka/Ks statistics
    for each functional domain of DDX11.
    (D) Comparison
    of DDX11 expression levels between different species.

    Fig.
    4 Proportional distribution of preferred expression genes at different stages of brain development
    .
    Functional description of biological pathways
    that should significantly enrich high-expression genes at the stage of brain development.



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