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基于整合网络的方法鉴定慢性阻塞性肺疾病中的驱动基因群落。

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

机构信息

GSK, Siena, Italy.

Università di Torino, Torino, Italy.

出版信息

NPJ Syst Biol Appl. 2024 Oct 26;10(1):125. doi: 10.1038/s41540-024-00425-6.

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.

摘要

慢性阻塞性肺疾病(COPD)是一种病因复杂的疾病,其特征为急性加重和稳定期。我们旨在使用在 AERIS 临床研究(NCT01360398)中收集的全血样本,鉴定特定 COPD 情况下调节的生物学功能。所考虑的情况是加重发作、气道阻塞严重程度以及痰液样本中呼吸道病原体的存在。采用整合的多网络基因社区检测(MNGCD)方法,我们分析了表达谱以鉴定相关基因的社区。该方法为每个探索的条件/样本子集结合了不同的基因相互作用层:基因表达相似性、蛋白质-蛋白质相互作用、转录因子和经实验验证的 microRNA 调控物。在加重期和稳定期就诊的患者中,血红素代谢、干扰素-α和干扰素-γ途径都受到了调节,但涉及不同的基因集。在加重期,一个重要的基因社区富含 G2M 检查点、E2F 靶标和有丝分裂纺锤体途径。TAL1 调节剂和 hsa-let-7b-5p microRNA 的靶标随着气道阻塞严重程度的增加而受到调节。莫拉氏菌属和流感嗜血杆菌等细菌感染在急性加重期引发了特定的细胞和炎症反应,表明宿主对感染有积极反应。总之,COPD 是一种复杂的多因素疾病,需要深入研究其在发病过程中的病因和特征,全血转录组谱分析可以有助于捕捉与该疾病相关的一些相关调节机制。在这项工作中,我们探索了整合多种调节基因网络的多网络建模,增强了我们对 COPD 发病机制中涉及的生物学功能的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73b9/11513021/0d83fb95ac68/41540_2024_425_Fig1_HTML.jpg

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