Coleman Laura A, Khoo Siew-Kim, Franks Kimberley, Prastanti Franciska, Le Souëf Peter, Karpievitch Yuliya V, Laing Ingrid A, Bosco Anthony
Medical School (Paediatrics), University of Western Australia, Perth, WA 6009, Australia.
Telethon Kids Institute, University of Western Australia, Perth, WA 6009, Australia.
J Pers Med. 2021 Dec 3;11(12):1293. doi: 10.3390/jpm11121293.
Human rhinovirus (RV)-induced exacerbations of asthma and wheeze are a major cause of emergency room presentations and hospital admissions among children. Previous studies have shown that immune response patterns during these exacerbations are heterogeneous and are characterized by the presence or absence of robust interferon responses. Molecular phenotypes of asthma are usually identified by cluster analysis of gene expression levels. This approach however is limited, since genes do not exist in isolation, but rather work together in networks. Here, we employed personal network inference to characterize exacerbation response patterns and unveil molecular phenotypes based on variations in network structure. We found that personal gene network patterns were dominated by two major network structures, consisting of interferon-response versus FCER1G-associated networks. Cluster analysis of these structures divided children into subgroups, differing in the prevalence of atopy but not RV species. These network structures were also observed in an independent cohort of children with virus-induced asthma exacerbations sampled over a time course, where we showed that the FCER1G-associated networks were mainly observed at late time points (days four-six) during the acute illness. The ratio of interferon- and FCER1G-associated gene network responses was able to predict recurrence, with low interferon being associated with increased risk of readmission. These findings demonstrate the applicability of personal network inference for biomarker discovery and therapeutic target identification in the context of acute asthma which focuses on variations in network structure.
人鼻病毒（RV）诱发的哮喘和喘息加重是儿童急诊就诊和住院的主要原因。先前的研究表明，这些加重期的免疫反应模式是异质性的，其特征是存在或不存在强烈的干扰素反应。哮喘的分子表型通常通过基因表达水平的聚类分析来确定。然而，这种方法是有限的，因为基因并非孤立存在，而是在网络中协同作用。在这里，我们采用个人网络推理来表征加重期反应模式，并基于网络结构的变化揭示分子表型。我们发现个人基因网络模式主要由两种主要的网络结构主导，即干扰素反应网络与FCER1G相关网络。对这些结构进行聚类分析，可将儿童分为不同亚组，这些亚组在特应性患病率上存在差异，但在RV种类上无差异。在一个独立的、随时间进程采样的病毒诱发哮喘加重儿童队列中也观察到了这些网络结构，我们发现FCER1G相关网络主要在急性疾病的后期（第4 - 6天）出现。干扰素相关基因网络反应与FCER1G相关基因网络反应的比率能够预测复发情况，低干扰素水平与再入院风险增加相关。这些发现证明了个人网络推理在急性哮喘背景下用于生物标志物发现和治疗靶点识别的适用性，该方法侧重于网络结构的变化。
