Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability

Cell function studies primarily focus on measuring overall molecular abundances while often overlooking critical clues—including protein modifications and molecular interaction networks—that critically determine the functional properties of the cell. In prior work, we introduced a suite of methods to reveal context-specific transcription factor-gene regulatory networks, kinase-substrate networks, and protein interaction networks and leveraged them to gain deeper insights into transcriptional regulation and signal transduction. However, the complex interdependencies between these networks are still elusive. To address this challenge, we introduce a multi-omics framework, aimed at harnessing measured or inferred protein activity in context-specific networks, which yields deeper functional insights into mechanisms underlying molecular phenotypes, compared to protein abundance alone. As proof of concept, we utilized progressively differentiated instances of HeLa CCL2 and Kyoto cell lines to explore the role of protein complexes and interactions in cell doubling time and susceptibility to Salmonella Typhimurium infection. Notably, this analysis underscores the pivotal role of protein interaction networks in linking molecular profiles to phenotypic outcomes, thus providing a highly generalizable framework for multi-omics dataset analysis.细胞功能研究多以检测整体分子丰度为核心，却往往忽略了决定细胞功能属性的关键线索——包括蛋白质修饰与分子互作网络。在既往研究中，我们曾提出一套方法，用于揭示情境特异性转录因子-基因调控网络、激酶-底物网络与蛋白质互作网络，并借助这些方法对转录调控与信号转导过程获得了更深入的认知。然而，这些网络之间复杂的互作关联仍未被充分阐明。为应对这一挑战，我们提出了一种多组学框架，旨在利用情境特异性网络中实测或推断得到的蛋白质活性，相较于仅基于蛋白质丰度的分析，该框架能够为分子表型背后的机制提供更深入的功能层面认知。作为概念验证，我们利用逐步分化的HeLa CCL2与Kyoto细胞系，探究了蛋白质复合物与互作在细胞倍增时间及鼠伤寒沙门氏菌感染易感性中的作用。值得注意的是，本分析凸显了蛋白质互作网络在将分子特征与表型结果关联起来过程中的关键作用，从而为多组学数据集分析提供了一个通用性极强的研究框架。