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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.

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Most successful firms have an abundance of new and old knowledge in their research and development laboratories, and only a fraction is being put into use in new product development. This knowledge is left over from projects that have been killed at different development stages and may actually carry considerable value. In this article, we propose a knowledge bank as a possible solution to preserve and possibly grow this knowledge. It is a self-sustaining institute with minimal or no ongoing effort from the donor company, yet manages the knowledge in a way that protects proprietary interests and actively fosters communication and interchange among sponsoring companies wherever possible. The framework of this structure, as well as how it works, is described here. Specifically, a system dynamics modeling of the knowledge bank is developed, and a simulation study is conducted using VENSIM®. The results confirm the viability of creating such a system in a consortium of organizations. [ABSTRACT FROM AUTHOR]

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.