A meta-analysis of the transcriptional landscape of end stage heart failure with reduced ejection fraction. (25th November 2020)

• Record Type: Journal Article
• Authors: Lanzer, J; Ramirez-Flores, R; Schultz, J.H; Levinson, R; Saez-Rodriguez, J
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Basic Science - Cardiac Diseases:Heart Failure; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.3676
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: : During heart failure (HF) the myocardium undergoes ventricular remodeling, which is accompanied by changes in gene expression. Transcriptomic studies have contributed to fundamental knowledge of this remodeling. However many studies do not agree on the differentially expressed marker genes. We hypothesized that a comprehensive meta-analysis of available studies would help to address this incongruence. We curated and uniformly processed 14 public transcriptomic data sets consisting of left ventricular samples from 265 healthy and 642 failing hearts. We assessed their comparability by applying transfer learning based classifiers. We then combined all datasets to extract a consensus gene signature. Combining this consensus signature with biological prior knowledge, we estimated transcription factor activities with DoRothEA and pathway activities by footprint analysis with PROGENy. Although single studies reported highly dissimilar marker genes, transfer knowledge based approaches revealed that disease patterns are conserved across studies. We derived a gene ranking that reflects consistent molecular hallmarks of HF. The ranking is strongly enriched in fibrosis related gene sets and contains significant (all p-values <0.05) footprints of numerous transcription factors including Nanog, Sox2, Pbx3, Mef2; pathways including Jak-Stat and miRNAs including MIR-206, MIR-514. We demonstrated the feasibility of combining transcriptional studies from different technologies, years and centers. We extracted a consensus gene signature as a valuable resource to understand common molecular patterns in HF. To our knowledge, this report represents the largest meta-analysis of transcriptional HF studies to date. Funding Acknowledgement: Type of funding source: Foundation. Main funding source(s): Klaus Tschirer Stiftung