Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.
Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.
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网络模型预测患者血清培养的瓣膜肌成纤维细胞的个性化基因表达和药物反应。
DOI：
10.1073/pnas.2117323119
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发表时间：
2022-02-22
影响因子：
11.1
通讯作者：
Richardson WJ
中科院分区：
文献类型：
--
作者：
Rogers JD;Aguado BA;Watts KM;Anseth KS;Richardson WJ
心脏瓣膜疾病的一个主要原因是瓣膜中瘢痕样组织的过度积聚，这会阻碍瓣膜打开和关闭的能力，并最终导致心力衰竭。控制这种瘢痕组织重塑是非常困难的，部分原因是复杂的细胞调节系统，部分原因是不同患者之间的差异很大。我们已经建立了一个调节瓣膜重塑的细胞生化网络的计算模型，该模型可以根据患者特定的生化水平虚拟预测瓣膜瘢痕形成。通过这个模型，我们进行了个性化的药物筛选，以预测每个患者对特定疗法的反应，后续的细胞培养实验验证了我们的预测，准确率超过80%。主动脉瓣狭窄（AVS）患者由于过度的细胞外基质（ECM）重塑而经历致病性瓣叶硬化。许多微环境因素影响ECM重塑基因在组织驻留瓣膜肌成纤维细胞中的致病性表达，并且复杂的肌成纤维细胞信号传导网络的调节取决于患者特异性细胞外因子。在这里，我们将手动策划的肌成纤维细胞信号传导网络与数据驱动的转录因子网络相结合，以预测患者特异性肌成纤维细胞基因表达特征和药物反应。使用来自与AVS患者血清一起培养的肌成纤维细胞的转录组学数据，我们产生了一个大规模的逻辑门控微分方程模型，其中通过334个信号传导和转录反应的网络转导11个生化和生物力学信号，以准确预测27个纤维化相关基因的表达。个性化模型预测的基因表达与AVS患者超声心动图数据之间存在相关性，表明纤维化相关信号传导与患者特异性AVS严重程度之间存在联系。此外，全局网络扰动分析揭示了对网络范围活性影响最大的信号分子，包括内皮素1（ET 1）、白细胞介素6（IL 6）和转化生长因子β（TGFβ），沿着下游介质c-Jun N-末端激酶（JNK）、信号转导和转录激活因子（STAT）和活性氧（ROS）。最后，我们进行了虚拟药物筛选，以确定患者特异性的药物反应，这是通过在瓣膜间质细胞与AVS患者血清培养和治疗或不治疗波生坦（一种临床批准的ET 1受体抑制剂）的纤维化基因表达测量实验验证。总之，我们的工作提高了计算方法的能力，为临床决策提供了机械基础，包括患者分层和个性化药物筛选。
A major contributor to heart valve disease is the excessive buildup of scar-like tissue in the valve, which can hinder the ability of the valve to open and close and can ultimately lead to heart failure. Controlling this scar tissue remodeling is very difficult due, in part, to a complex cellular regulation system and, in part, to large variabilities between different patients. We have built a computational model of the cell biochemical network that regulates valve remodeling, which enables virtual predictions of valve scarring given patient-specific biochemical levels. With this model, we ran personalized drug screens to predict each patient’s response to particular therapies, and follow-up cell culture experiments validated our predictions with over 80% accuracy. Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan—a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.
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DOI：
10.1161/atvbaha.120.315261
发表时间：
2020-11
期刊：
Arteriosclerosis, thrombosis, and vascular biology
影响因子：
--
作者：
Grim JC;Aguado BA;Vogt BJ;Batan D;Andrichik CL;Schroeder ME;Gonzalez-Rodriguez A;Yavitt FM;Weiss RM;Anseth KS
通讯作者：
Anseth KS
影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
DOI：
10.1115/1.2801670
发表时间：
2007-12-01
影响因子：
1.7
作者：
Huang, Hsiao-Ying Shadow;Liao, Jun;Sacks, Michael S.
通讯作者：
Sacks, Michael S.
影响因子：
10.8
作者：
Hagler, Michael A.;Hadley, Thomas M.;Miller, Jordan D.
通讯作者：
Miller, Jordan D.
影响因子：
37.8
作者：
Lacraz, Gregory P. A.;Junker, Jan Philipp;Van Rooij, Eva
通讯作者：
Van Rooij, Eva