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转录组分析炎症性心肌病确定疾病的分子特征,并为基于网络的治疗理由提供预测。

Transcriptomic Analysis of Inflammatory Cardiomyopathy Identifies Molecular Signatures of Disease and Informs Prediction of a Network-Based Rationale for Therapy.

机构信息

RDM Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom.

Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.

出版信息

Front Immunol. 2021 Mar 5;12:640837. doi: 10.3389/fimmu.2021.640837. eCollection 2021.

Abstract

Inflammatory cardiomyopathy covers a group of diseases characterized by inflammation and dysfunction of the heart muscle. The immunosuppressive agents such as prednisolone, azathioprine and cyclosporine are modestly effective treatments, but a molecular rationale underpinning such therapy or the development of new therapeutic strategies is lacking. We aimed to develop a network-based approach to identify therapeutic targets for inflammatory cardiomyopathy from the evolving myocardial transcriptome in a mouse model of the disease. We performed bulk RNA sequencing of hearts at early, mid and late time points from mice with experimental autoimmune myocarditis. We identified a cascade of pathway-level events involving early activation of cytokine and chemokine-signaling pathways that precede leucocyte infiltration and are followed by innate immune, antigen-presentation, complement and cell-adhesion pathway activation. We integrated these pathway events into a network-like representation from which we further identified a 50-gene subnetwork that is predominantly induced during the course of autoimmune myocardial inflammation. We developed a combinatorial attack strategy where we quantify network tolerance to combinatorial node removal to determine target-specific therapeutic potential. We find that combinatorial attack of , and disconnects 80% of nodes from the largest network component. Two of these nodes, and , are directly targeted by prednisolone and azathioprine respectively, supporting the idea that the methodology developed here can identify valid therapeutic targets. Whereas and removal disconnects 56% of nodes, we show that additional removal of and causes 72% node disconnection. In conclusion, transcriptome profiling, pathway integration, and network identification of autoimmune myocardial inflammation provide a molecular signature applicable to the diagnosis of inflammatory cardiomyopathy. Combinatorial attack provides a rationale for immunosuppressive therapy of inflammatory cardiomyopathy and provides an prediction that the approved therapeutics, ibrutinib and idelalisib targeting and respectively, could potentially be re-purposed as adjuncts to immunosuppression.

摘要

炎性心肌病涵盖了一组以心肌炎症和功能障碍为特征的疾病。免疫抑制剂如泼尼松、硫唑嘌呤和环孢素是适度有效的治疗方法,但缺乏这种治疗的分子基础或新治疗策略的发展。我们旨在从疾病的小鼠模型的不断发展的心肌转录组中,开发一种基于网络的方法来鉴定炎性心肌病的治疗靶点。我们对实验性自身免疫性心肌炎小鼠的心脏在早期、中期和晚期进行了批量 RNA 测序。我们确定了一系列涉及细胞因子和趋化因子信号通路早期激活的途径级联事件,这些事件先于白细胞浸润,并随后激活固有免疫、抗原呈递、补体和细胞黏附途径。我们将这些途径事件整合到一个类似网络的表示中,从中进一步确定了一个主要在自身免疫性心肌炎症过程中诱导的 50 个基因子网。我们开发了一种组合攻击策略,我们量化网络对组合节点去除的耐受性,以确定针对特定目标的治疗潜力。我们发现,对 和 的组合攻击可将最大网络组件中的 80%的节点断开连接。这两个节点中的 和 分别被泼尼松和硫唑嘌呤直接靶向,支持这里开发的方法可以识别有效的治疗靶点的想法。而 和 的去除会断开 56%的节点,我们表明,额外去除 和 会导致 72%的节点断开连接。总之,自身免疫性心肌炎症的转录组分析、途径整合和网络识别为炎性心肌病的诊断提供了一个分子特征。组合攻击为炎性心肌病的免疫抑制治疗提供了一个原理,并提供了一个预测,即分别针对 和 的已批准治疗药物,伊布替尼和idelalisib,可能被重新用作免疫抑制的辅助治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e0c/7973371/ccaa0796a808/fimmu-12-640837-g0001.jpg

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