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Abstract：
Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.
摘要：
背景转录组学研究为人类心力衰竭（HF）中心肌重塑的基本知识做出了贡献。然而,报道的关键HF基因通常在研究之间不一致，缺乏系统的努力来整合来自多个患者队列的证据。这里,我们的目标是提供一个框架,用于全面比较和分析公开数据集,从而获得人类终末期HF的无偏一致转录签名.方法和结果我们策划并统一处理了来自263个健康和653个衰竭人类心脏的左心室样本的16个公共转录组学研究。首先,我们通过使用线性分类器和过度表达分析评估了研究之间的一致性程度.然后，我们对14.041个基因的失调进行了荟萃分析,以提取HF的共有特征.最后,为了在功能上表征这个签名，我们估计了343个转录因子的活性，14个信号通路，和182个微小RNA，以及5998个生物过程的富集。机器学习方法揭示了所有研究中保守的疾病模式，独立于技术差异。这些一致的分子变化是通过荟萃分析优先考虑的，在外部数据上进行功能表征和验证。我们在免费的公共资源（https://saezlab）中提供所有结果。shinyapps.io/reheat/），并通过破译胎儿基因重编程和追踪HF患者血浆蛋白质组标志物的潜在心肌起源来举例说明用法。结论尽管技术和采样变异性混淆了个体研究中差异表达基因的鉴定，我们证明了在终末期HF期间协调的分子反应是保守的。所提供的资源对于补充独立研究的发现和破译衰竭心肌的基本变化至关重要。
