Department of Pharmacology and Personalised Medicine, Maastricht University, Maastricht, The Netherlands.
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Handb Exp Pharmacol. 2021;264:49-68. doi: 10.1007/164_2020_386.
Most diseases are defined by a symptom, not a mechanism. Consequently, therapies remain symptomatic. In reverse, many potential disease mechanisms remain in arbitrary search for clinical relevance. Reactive oxygen species (ROS) are such an example. It is an attractive hypothesis that dysregulation of ROS can become a disease trigger. Indeed, elevated ROS levels of various biomarkers have been correlated with almost every disease, yet after decades of research without any therapeutic application. We here present a first systematic, non-hypothesis-based approach to transform this field as a proof of concept for biomedical research in general. We selected as seed proteins 9 families with 42 members of clinically researched ROS-generating enzymes, ROS-metabolizing enzymes or ROS targets. Applying an unbiased network medicine approach, their first neighbours were connected, and, based on a stringent subnet participation degree (SPD) of 0.4, hub nodes excluded. This resulted in 12 distinct human interactome-based ROS signalling modules, while 8 proteins remaining unconnected. This ROSome is in sharp contrast to commonly used highly curated and integrated KEGG, HMDB or WikiPathways. These latter serve more as mind maps of possible ROS signalling events but may lack important interactions and often do not take different cellular and subcellular localization into account. Moreover, novel non-ROS-related proteins were part of these forming functional hybrids, such as the NOX5/sGC, NOX1,2/NOS2, NRF2/ENC-1 and MPO/SP-A modules. Thus, ROS sources are not interchangeable but associated with distinct disease processes or not at all. Module members represent leads for precision diagnostics to stratify patients with specific ROSopathies for precision intervention. The upper panel shows the classical approach to generate hypotheses for a role of ROS in a given disease by focusing on ROS levels and to some degree the ROS type or metabolite. Low levels are considered physiological; higher amounts are thought to cause a redox imbalance, oxidative stress and eventually disease. The source of ROS is less relevant; there is also ROS-induced ROS formation, i.e. by secondary sources (see upwards arrow). The non-hypothesis-based network medicine approach uses genetically or otherwise validated risk genes to construct disease-relevant signalling modules, which will contain also ROS targets. Not all ROS sources will be relevant for a given disease; some may not be disease relevant at all. The three examples show (from left to right) the disease-relevant appearance of an unphysiological ROS modifier/toxifier protein, ROS target or ROS source.
大多数疾病都是由症状定义的，而不是由机制定义的。因此，治疗方法仍然是对症的。相反，许多潜在的疾病机制仍然在任意寻找临床相关性。活性氧（ROS）就是一个例子。ROS 失调可能成为疾病触发因素，这是一个有吸引力的假设。事实上，各种生物标志物的 ROS 水平升高与几乎所有疾病都有关，但经过几十年的研究，没有任何治疗应用。我们在这里提出了一种基于非假设的系统方法，以将该领域转变为一般生物医学研究的概念验证。我们选择了 9 个家族的 42 个具有临床研究的 ROS 生成酶、ROS 代谢酶或 ROS 靶标的种子蛋白。应用无偏网络医学方法，连接它们的第一个邻居，并根据严格的子网参与度（SPD）为 0.4 排除枢纽节点。这导致了 12 个独特的基于人类相互作用组的 ROS 信号模块，而 8 个蛋白仍然没有连接。这与常用的高度编辑和集成的 KEGG、HMDB 或 WikiPathways 形成鲜明对比。后者更像是可能的 ROS 信号事件的思维导图，但可能缺少重要的相互作用，并且通常不考虑不同的细胞和亚细胞定位。此外，新型非 ROS 相关蛋白是形成功能杂合体的一部分，例如 NOX5/sGC、NOX1、2/NOS2、NRF2/ENC-1 和 MPO/SP-A 模块。因此，ROS 来源不可互换，但与不同的疾病过程相关或根本不相关。模块成员代表用于精准诊断的先导物，可对具有特定 ROS 病的患者进行分层，以便进行精准干预。上图显示了通过关注 ROS 水平并在某种程度上关注 ROS 类型或代谢物来生成 ROS 在特定疾病中起作用的假设的经典方法。低水平被认为是生理性的；较高的水平被认为会导致氧化还原失衡、氧化应激，最终导致疾病。ROS 的来源不太重要；也存在由次级来源引起的 ROS 诱导的 ROS 形成（见向上箭头）。基于非假设的网络医学方法使用遗传或其他方式验证的风险基因来构建与疾病相关的信号模块，其中还将包含 ROS 靶标。并非所有的 ROS 来源都与特定疾病相关；有些可能与疾病根本无关。三个例子从左到右显示了一种非生理性 ROS 修饰剂/毒物蛋白、ROS 靶标或 ROS 来源在疾病中的出现。
