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Analysis of two mechanisms of telomere maintenance based on the theory of g-Networks and stochastic automata networks.
BMC Genomics ( IF 4.4 ) Pub Date : 2020-09-09 , DOI: 10.1186/s12864-020-06937-9
Kyung Hyun Lee 1 , Marek Kimmel 1, 2
Affiliation  

Background Telomeres, which are composed of repetitive nucleotide sequences at the end of chromosomes, behave as a division clock that measures replicative senescence. Under the normal physiological condition, telomeres shorten with each cell division, and cells use the telomere lengths to sense the number of divisions. Replicative senescence has been shown to occur at approximately 50–70 cell divisions, which is termed the Hayflick’s limit. However, in cancer cells telomere lengths are stabilized, thereby allowing continual cell replication by two known mechanisms: activation of telomerase and Alternative Lengthening of Telomeres (ALT). The connections between the two mechanisms are complicated and still poorly understood. Results In this research, we propose that two different approaches, G-Networks and Stochastic Automata Networks, which are stochastic models motivated by queueing theory, are useful to identify a set of genes that play an important role in the state of interest and to infer their previously unknown correlation by obtaining both stationary and joint transient distributions of the given system. Our analysis using G-Network detects five statistically significant genes (CEBPA, FOXM1, E2F1, c-MYC, hTERT) with either mechanism, contrasted to normal cells. A new algorithm is introduced to show how the correlation between two genes of interest varies in the transient state according not only to each mechanism but also to each cell condition. Conclusions This study expands our existing knowledge of genes associated with mechanisms of telomere maintenance and provides a platform to understand similarities and differences between telomerase and ALT in terms of the correlation between two genes in the system. This is particularly important because telomere dynamics plays a major role in many physiological and disease processes, including hematopoiesis.

中文翻译:

基于g网络和随机自动机网络理论分析两种端粒维持机制。

背景端粒由染色体末端的重复核苷酸序列组成,起着测量复制衰老的分裂钟的作用。在正常的生理条件下,端粒随着每个细胞分裂而缩短,并且细胞利用端粒的长度来感知分裂的次数。已经显示复制衰老发生在大约50-70个细胞分裂处,这被称为Hayflick极限。然而,在癌细胞中,端粒的长度是稳定的,从而通过两种已知的机制允许细胞的连续复制:端粒酶的激活和端粒的替代性延长(ALT)。两种机制之间的联系非常复杂,但了解仍然很少。结果在这项研究中,我们提出了两种不同的方法,即G网络和随机自动机网络,它们是受排队论启发的随机模型,可用于识别一组在感兴趣状态中起重要作用的基因,并通过获取给定系统的平稳和联合瞬时分布来推断其先前未知的相关性。与正常细胞相比,我们使用G-Network进行的分析可检测到5种具有统计学意义的基因(CEBPA,FOXM1,E2F1,c-MYC,hTERT),其中任一机制均可。引入了一种新算法,以显示两个目标基因之间的相关性如何不仅根据每种机制而且还根据每种细胞条件在瞬时状态下发生变化。结论这项研究扩展了我们对端粒维持机制相关基因的现有知识,并提供了一个平台,以了解系统中两个基因之间的相关性,从而了解端粒酶和ALT之间的异同。这一点尤其重要,因为端粒动力学在包括血细胞生成在内的许多生理和疾病过程中起着重要作用。
更新日期:2020-09-08
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