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遗传网络编码着它们过去的秘密。

Genetic networks encode secrets of their past.

机构信息

Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.

Institute for Quantum Science and Technology, University of Calgary, Alberta T2N 1N4, Canada.

出版信息

J Theor Biol. 2022 May 21;541:111092. doi: 10.1016/j.jtbi.2022.111092. Epub 2022 Mar 17.

Abstract

Research shows that gene duplication followed by either repurposing or removal of duplicated genes is an important contributor to evolution of gene and protein interaction networks. We aim to identify which characteristics of a network can arise through this process, and which must have been produced in a different way. To model the network evolution, we postulate vertex duplication and edge deletion as evolutionary operations on graphs. Using the novel concept of an ancestrally distinguished subgraph, we show how features of present-day networks require certain features of their ancestors. In particular, ancestrally distinguished subgraphs cannot be introduced by vertex duplication. Additionally, if vertex duplication and edge deletion are the only evolutionary mechanisms, then a graph's ancestrally distinguished subgraphs must be contained in all of the graph's ancestors. We analyze two experimentally derived genetic networks and show that our results accurately predict lack of large ancestrally distinguished subgraphs, despite this feature being statistically improbable in associated random networks. This observation is consistent with the hypothesis that these networks evolved primarily via vertex duplication. The tools we provide open the door for analyzing ancestral networks using current networks. Our results apply to edge-labeled (e.g. signed) graphs which are either undirected or directed.

摘要

研究表明,基因复制后,要么重新利用,要么删除重复的基因,这是基因和蛋白质相互作用网络进化的一个重要因素。我们的目的是确定哪些网络特征可以通过这个过程产生,而哪些特征必须以不同的方式产生。为了对网络进化进行建模,我们假设顶点复制和边删除是图的进化操作。使用一个新颖的概念,即祖先区分的子图,我们展示了当今网络的特征如何需要其祖先的某些特征。特别是,祖先区分的子图不能通过顶点复制引入。此外,如果顶点复制和边删除是唯一的进化机制,那么一个图的祖先区分的子图必须包含在该图的所有祖先中。我们分析了两个实验得出的遗传网络,并表明尽管在相关的随机网络中,这一特征在统计上不太可能,但我们的结果准确地预测了不存在大的祖先区分的子图。这一观察结果与这些网络主要通过顶点复制进化的假设是一致的。我们提供的工具为使用当前网络分析祖先网络打开了大门。我们的结果适用于无向或有向的带边标签(如符号)的图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b17/9037300/ea93b523b335/nihms-1792371-f0001.jpg

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本文引用的文献

1
On the Origin of Biomolecular Networks.生物分子网络的起源
Front Genet. 2019 Apr 10;10:240. doi: 10.3389/fgene.2019.00240. eCollection 2019.
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Origin of the Yeast Whole-Genome Duplication.酵母全基因组复制的起源
PLoS Biol. 2015 Aug 7;13(8):e1002221. doi: 10.1371/journal.pbio.1002221. eCollection 2015 Aug.
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Ohno's dilemma: evolution of new genes under continuous selection.大野的困境:持续选择下新基因的进化
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