Kim Kwansoo, Han Manyoung, Lee Doheon
Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
Comput Struct Biotechnol J. 2025 Jan 10;27:333-345. doi: 10.1016/j.csbj.2025.01.003. eCollection 2025.
Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.
组织间通信器（ITCs）是我们身体复杂且至关重要的组成部分，因为它们是体内稳态平衡的维持者。组织间交换的失调是各种疾病的核心，这并不奇怪。在这些疾病中，自身免疫性疾病（AIDs）是指一系列病理状况，其中通信失误导致免疫系统错误地攻击自身身体。由于其众多且多样的病理生理学特征，自身免疫性疾病难以轻易诊断和治疗，因此需要持续努力寻找潜在的诊断标志物或治疗靶点。因此，识别在疾病状态中显著参与的组织间通信器至关重要。在此，我们提出了InTiCAR，即通过带重启的随机游走算法寻找自身免疫性疾病的组织间通信器，这是一种基于网络探索的分析方法，它基于疾病基因的先验知识提出疾病特异性的组织间通信器，而无需外部表达数据。我们首先表明，InTiCAR可以为各种疾病获取不同的组织间通信器图谱。我们进一步说明，特别是对于自身免疫性疾病（AIDs），在使用英国生物银行血浆蛋白质组数据集诊断患者时，疾病特异性的组织间通信器比疾病基因表现更优。此外，通过CMap LINCS数据集，我们发现疾病特异性的组织间通信器可以观察到对自身免疫性疾病基因的高度干扰。我们的结果通过关注细胞外通信实体，为生物网络分析提供并突出了独特的视角。
