Quantifying pleiotropy through directed signaling networks: A synchronous Boolean network approach and in-silico pleiotropic scoring

IF 2 4区 生物学 Q2 BIOLOGY
Muhammad Mazhar Fareed , Sergey Shityakov
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引用次数: 0

Abstract

Pleiotropy refers to a gene's ability to influence multiple phenotypes or traits. In the context of human genetic diseases, pleiotropy manifests as different pathological effects resulting from mutations in the same gene. This phenomenon plays a crucial role in understanding gene–gene interactions in system-level biological diseases. Previous studies have largely focused on pleiotropy within undirected molecular correlation networks, leaving a gap in examining pleiotropy induced by directed signaling networks, which can better explain dynamic gene–gene interactions. In this study, we utilized a synchronous Boolean network model to explore pleiotropic dynamics induced by various mutations in large-scale networks. We introduced an in-silico Pleiotropic Score (sPS) to quantify the impact of gene mutations and validated the model against observational pleiotropy data from the Human Phenotype Ontology (HPO). Our results indicate a significant correlation between sPS and network structural characteristics, including degree centrality and feedback loop involvement. The highest correlation was observed between closeness centrality and sPS (0.6), suggesting that genes more central in the network exhibit higher pleiotropic potential. Furthermore, genes involved in feedback loops demonstrated higher sPS values (p < 0.0001), supporting the role of feedback loops in amplifying pleiotropic behavior. Our model provides a novel approach for quantifying pleiotropy through directed network dynamics, complementing traditional observational methods.
通过定向信令网络量化多效性:一种同步布尔网络方法和计算机多效性评分。
多效性指的是一个基因影响多种表型或性状的能力。在人类遗传疾病的背景下,多效性表现为同一基因突变引起的不同病理效应。这一现象在理解系统级生物疾病中基因-基因相互作用方面起着至关重要的作用。以往的研究主要集中在非定向分子相关网络中的多效性,对定向信号网络诱导的多效性的研究存在空白,而定向信号网络可以更好地解释基因与基因的动态相互作用。在这项研究中,我们利用一个同步布尔网络模型来探索大规模网络中各种突变引起的多效性动力学。我们引入了一个硅多效性评分(sPS)来量化基因突变的影响,并根据来自人类表型本体(HPO)的观察多效性数据验证了该模型。我们的研究结果表明,sp与网络结构特征,包括度中心性和反馈回路参与之间存在显著的相关性。密切中心性与sPS之间的相关性最高(0.6),这表明在网络中处于中心位置的基因具有更高的多效性潜力。此外,参与反馈回路的基因表现出更高的sPS值(p < 0.0001),支持反馈回路在放大多效性行为中的作用。我们的模型提供了一种通过定向网络动力学量化多效性的新方法,补充了传统的观察方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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