体细胞和种系突变率在进化过程中的相互作用

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI:10.1146/annurev-biodatasci-102523-104225
Annabel C Beichman, Luke Zhu, Kelley Harris
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引用次数: 0

摘要

新的测序技术使测量体细胞系突变率变得越来越可能。精确的种系突变率测量技术也已问世十年,这使得评估这一基本进化参数在整个生命树中的变化情况成为可能。在此,我们回顾了一些关于种系和体细胞突变率进化的经典理论,这些理论是利用群体遗传学和衰老与癌症生物学原理提出的。我们发现,体细胞突变率的测量结果虽然在系统发育多样性方面仍然有限,但似乎与保护体细胞的选择与寿命成正比的理论相一致。然而,生殖细胞理论和体细胞理论在预测哪个物种的 DNA 修复最准确方面存在冲突。要解决这一矛盾,需要仔细测量突变率如何随时间和细胞分裂而变化,并更好地了解细胞类型之间的突变率褶积性。
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The Evolutionary Interplay of Somatic and Germline Mutation Rates.

Novel sequencing technologies are making it increasingly possible to measure the mutation rates of somatic cell lineages. Accurate germline mutation rate measurement technologies have also been available for a decade, making it possible to assess how this fundamental evolutionary parameter varies across the tree of life. Here, we review some classical theories about germline and somatic mutation rate evolution that were formulated using principles of population genetics and the biology of aging and cancer. We find that somatic mutation rate measurements, while still limited in phylogenetic diversity, seem consistent with the theory that selection to preserve the soma is proportional to life span. However, germline and somatic theories make conflicting predictions regarding which species should have the most accurate DNA repair. Resolving this conflict will require carefully measuring how mutation rates scale with time and cell division and achieving a better understanding of mutation rate pleiotropy among cell types.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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