An integrative network-based approach to identify driving gene communities in chronic obstructive pulmonary disease.

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Roberta Marino, Yousef El Aalamat, Vanesa Bol, Michele Caselle, Giuseppe Del Giudice, Christophe Lambert, Duccio Medini, Tom M A Wilkinson, Alessandro Muzzi
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引用次数: 0

Abstract

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa-let-7b - 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis.

基于网络的综合方法识别慢性阻塞性肺病的驱动基因群。
慢性阻塞性肺疾病(COPD)是一种病因复杂的疾病,其特点是急性加重期和稳定期。我们的目的是利用在 AERIS 临床研究(NCT01360398)中采集的全血样本,确定在特定 COPD 病症中调节的生物功能。考虑的情况包括病情恶化的开始、气道阻塞的严重程度以及痰液样本中是否存在呼吸道病原体。我们采用综合多网络基因群落检测(MNGCD)方法分析表达谱,以确定相关基因群落。该方法结合了每种探索条件/样本子集的不同基因相互作用层:基因表达相似性、蛋白质-蛋白质相互作用、转录因子和微RNA验证调控子。血红素代谢、干扰素-α和干扰素-γ通路在患者病情加重和病情稳定时都受到了调节,但涉及到不同的基因集。在病情恶化期间,G2M 检查点、E2F 靶点和有丝分裂纺锤体通路丰富了一个重要的基因群。TAL1调节因子和hsa-let-7b - 5p microRNA的靶点随着气道阻塞严重程度的增加而改变。卡他沙雷氏菌,尤其是流感嗜血杆菌的细菌感染在急性加重期引发了特定的细胞和炎症反应,表明宿主对感染的积极反应。总之,慢性阻塞性肺病是一种复杂的多因素疾病,需要深入研究其病因和演变过程中的特征,而全血转录组分析有助于捕捉与该疾病相关的一些调控机制。在这项工作中,我们探索了多网络建模,整合了不同层次的调控基因网络,提高了我们对慢性阻塞性肺病发病机理所涉及的生物功能的理解。
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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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