Consensus Transcriptional Landscape of Human End‐Stage Heart Failure
Autor: | Christian H. Holland, Florian Leuschner, Jan D. Lanzer, Ricardo O. Ramirez Flores, Julio Saez-Rodriguez, Rebecca T. Levinson, Jobst-Hendrik Schultz, Patrick Most |
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Rok vydání: | 2021 |
Předmět: |
Consensus
Computational biology 030204 cardiovascular system & hematology knowledge banks transcriptomics 03 medical and health sciences 0302 clinical medicine Text mining Humans Medicine 030304 developmental biology Heart Failure 0303 health sciences Ventricular Remodeling Systematic Review and Meta‐analysis business.industry Inflammatory Heart Disease Gene Expression Profiling Myocardium Chronic Ischemic Heart Disease Human heart medicine.disease Remodeling machine learning meta‐analysis Heart failure End stage heart failure consensus signature Transcriptome Cardiology and Cardiovascular Medicine business Signal Transduction Transcription Factors |
Zdroj: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
ISSN: | 2047-9980 |
Popis: | 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. |
Databáze: | OpenAIRE |
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