Department of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, Rome, 00128, Italy.
Center for Life Nano- & Neuro-Science, Italian Institute of Technology, Viale Regina Elena 291, Rome, 00161, Italy.
Sci Rep. 2024 Nov 12;14(1):27658. doi: 10.1038/s41598-024-79086-8.
Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible. This response functions as a dynamic signaling network, with proteins as nodes and their interactions as edges. We analyze these protein-protein networks across different organisms to understand their intricate intra-cellular interactions and behaviors. In this work, analyzing twelve organisms, we assess how fundamental measures in network theory can individuate seed proteins and specific pathways across organisms. We employ network robustness to evaluate and compare the strength of the investigated protein-protein interaction networks, and the structural controllability of complex networks to find and compare the sets of driver nodes necessary to control the overall networks. We find that network measures are related to phylogenetics, and advanced network methods can identify main pathways of significance in the complete Unfolded Protein Response mechanism.
生命系统依赖于协调的分子相互作用，特别是与基因表达和蛋白质活性相关的作用。 unfolded protein response (UPR) 是真核细胞中的一个关键机制，当未折叠的蛋白质超过临界阈值时被激活。它通过增强蛋白质折叠、启动质量控制以及在损伤不可逆转时激活降解途径来维持细胞内稳态。这种反应作为一个动态的信号网络，其中蛋白质作为节点，它们的相互作用作为边缘。我们分析了不同生物体中的这些蛋白质-蛋白质网络，以了解它们复杂的细胞内相互作用和行为。在这项工作中，我们分析了 12 种生物体，评估了网络理论中的基本度量如何在生物体之间识别种子蛋白和特定途径。我们利用网络鲁棒性来评估和比较所研究的蛋白质-蛋白质相互作用网络的强度，以及复杂网络的结构可控性，以找到和比较控制整个网络所需的驱动节点集。我们发现网络度量与系统发生学有关，并且高级网络方法可以识别完整 unfolded protein response 机制中重要的主要途径。
