利用可解释深度网络对婴儿支气管炎病理生物学进行综合组学分析

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Tadao Ooka, Naoto Usuyama, Ryohei Shibata, Michihito Kyo, Jonathan M Mansbach, Zhaozhong Zhu, Carlos A Camargo, Kohei Hasegawa
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引用次数: 0

摘要

支气管炎是婴儿住院治疗的主要原因。然而,驱动支气管炎病理生物学的分子网络仍然未知。包括转录组和代谢组在内的整合分子网络可以确定导致疾病严重程度的功能和调控途径。在此,我们整合了一项 17 个中心的前瞻性队列研究中 397 名因支气管炎住院的婴儿的鼻咽转录组和代谢组数据。利用可解释的深度网络模型,我们发现了一个由 401 个转录本和 38 个代谢物组成的全局集群(omics-cluster),它能区分支气管炎的严重程度(测试集 AUC 为 0.828)。这个全元素集群衍生出一个分子网络,在这个网络中,先天免疫相关代谢物(如神经酰胺)处于中心位置,并以收费样受体(TLR)和 NF-κB 信号通路(均为 FDR 2 类似物(如伊洛前列素),通过 TLR 信号促进抗炎作用)为特征。我们的方法不仅有助于确定婴儿支气管炎的分子网络,还有助于开发开创性的治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis.

Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis.

Bronchiolitis is the leading cause of infant hospitalization. However, the molecular networks driving bronchiolitis pathobiology remain unknown. Integrative molecular networks, including the transcriptome and metabolome, can identify functional and regulatory pathways contributing to disease severity. Here, we integrated nasopharyngeal transcriptome and metabolome data of 397 infants hospitalized with bronchiolitis in a 17-center prospective cohort study. Using an explainable deep network model, we identified an omics-cluster comprising 401 transcripts and 38 metabolites that distinguishes bronchiolitis severity (test-set AUC, 0.828). This omics-cluster derived a molecular network, where innate immunity-related metabolites (e.g., ceramides) centralized and were characterized by toll-like receptor (TLR) and NF-κB signaling pathways (both FDR < 0.001). The network analyses identified eight modules and 50 existing drug candidates for repurposing, including prostaglandin I2 analogs (e.g., iloprost), which promote anti-inflammatory effects through TLR signaling. Our approach facilitates not only the identification of molecular networks underlying infant bronchiolitis but the development of pioneering treatment strategies.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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