Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom.
Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2022 Jun 22;18(6):e1010112. doi: 10.1371/journal.pcbi.1010112. eCollection 2022 Jun.
Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.
细胞间通讯由许多可溶性介质介导，包括超过 40 种细胞因子。细胞因子，如 TNF、IL1β、IL5、IL6、IL12 和 IL23，是免疫介导的炎症性疾病（IMIDs）的重要治疗靶点，如炎症性肠病（IBD）、银屑病、哮喘、类风湿和青少年关节炎。鉴定出的细胞因子是炎症的因果驱动因素，而不仅仅是与炎症相关，这对于选择应在临床试验中研究的治疗靶点至关重要。由于细胞因子相互作用的体外模型提供了简化的框架来研究复杂的体内相互作用，并且可以很容易地进行实验干扰，因此它们是识别此类靶点的关键。我们提出了一种方法，可以从关于阻断单个细胞因子受体对其他细胞因子分泌率的影响的体外数据中提取最小加权细胞因子相互作用网络。现有的生物网络推断方法通常考虑基础数据集的相关结构，但这可能使它们不适合高度连接的非线性细胞因子相互作用数据。我们的方法使用常微分方程系统来表示细胞因子相互作用，并有效地计算出所有可能的网络配置中具有最低 Akaike 信息准则值的配置。它使我们能够研究间接细胞因子相互作用并量化抑制作用。提取的网络还可用于预测同时抑制各种细胞因子的组合效果。模型方程可以很容易地进行调整，以纳入更复杂的动态和适应时间数据。我们使用合成数据集验证了我们的方法，并将我们的方法应用于关于 IL23 调节的实验数据集，IL23 在银屑病和 IBD 中具有治疗相关性。我们根据未用于模型拟合的实验数据验证了几个模型预测。总之，我们提出了一种专门针对从 IMIDs 背景下的细胞因子扰动数据中有效推断细胞因子相互作用网络的新方法。
