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Proto-genes and de novo gene birth

机译:原始基因与新生基因的诞生

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摘要

Novel protein-coding genes can arise either through re-organization of pre-existing genes or denovo~(1,2). Processes involving re-organization of pre-existing genes, notably after gene duplication, have been extensively described~(1,2). In contrast, de novo gene birth remains poorly understood, mainly because translation of sequences devoid of genes, or 'non-genic' sequences, is expected to produce insignificant poly-peptides rather than proteins with specific biological functions~(1,3-6). Here we formalize an evolutionary model according to which functional genes evolve de novo through transitory proto-genes4 generated by widespread translational activity in non-genic sequences. Testing this model at the genome scale in Saccharomyces cerevisiae, we detect translation of hundreds of short species-specific open reading frames (ORFs) located in non-genic sequences. These translation events seem to provide adaptive potential~7, as suggested by their differential regulation upon stress and by signatures of retention by natural selection. In line with our model, we establish that S. cerevisiae ORFs can be placed within an evolutionary continuum ranging from non-genic sequences to genes. We identify ~1,900 candidate proto-genes among S. cerevisiae ORFs and find that de novo gene birth from such a reservoir may be more prevalent than sporadic gene duplication. Our work illustrates that evolution exploits seemingly dispensable sequences to generate adaptive functional innovation.
机译:新的蛋白质编码基因可以通过重组已有基因或denovo〜(1,2)而产生。 (1,2)已经广泛描述了涉及重组已有基因的过程,特别是在基因复制后。相反,从头基因的诞生仍然知之甚少,主要是因为缺乏基因的序列或“非基因”序列的翻译预期会产生微不足道的多肽,而不是具有特定生物学功能的蛋白质〜(1,3-6 )。在这里,我们对进化模型进行形式化,根据该模型,功能基因通过在非基因序列中普遍存在的翻译活性而产生的瞬时原基因4从头进化。在酿酒酵母的基因组规模上测试此模型,我们检测到位于非基因序列中的数百个短物种特异性开放阅读框(ORF)的翻译。这些翻译事件似乎提供了适应性电位〜7,这是由于它们对压力的差异调节和自然选择的保留特征所暗示的。根据我们的模型,我们确定酿酒酵母ORF可以置于从非基因序列到基因的进化连续体中。我们在酿酒酵母ORF中鉴定了约1,900个候选原始基因,并发现从这种储库中新生基因的诞生可能比散发的基因复制更为普遍。我们的工作表明,进化利用貌似可有可无的序列来产生自适应功能创新。

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  • 来源
    《Nature》 |2012年第7407期|p.370-374|共5页
  • 作者单位

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA,UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525, Computational and Mathematical Biology Group, Grenoble F-38041, France;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA;

    Department of Systems Biology,Harvard Medical School, Boston, Massachusetts 02115, USA;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA;

    Centerfor International Developmentand Harvard University, Cambridge, Massachusetts 02138, USA;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA,Laboratoire de Bioinformatique des Genomes et des Reseaux (BiGRe), Campus Plaine, Free University of Brussels, 1050 Brussels, Wallonia-Brussels Federation, Belgium;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA,Unit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Liege, 4000 Liege, Wai Ionia-Brussels Federation, Belgium;

    The MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA;

    Howard Hughes Medical Institute, Department of Cellular and Molecular Pharmacology, University of California, San Francisco, and California Institute for Quantitative Biosciences, San Francisco,California 94158, USA;

    Howard Hughes Medical Institute, Department of Cellular and Molecular Pharmacology, University of California, San Francisco, and California Institute for Quantitative Biosciences, San Francisco,California 94158, USA;

    Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA,Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology,Cambridge, Massachusetts 02139, USA;

    UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525, Computational and Mathematical Biology Group, Grenoble F-38041, France;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA;

    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA,Department of Genetics, Harvard Medical School,Boston, Massachusetts 02115, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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  • 入库时间 2022-08-18 02:54:13

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