Integrated Transcriptome Map Highlights Structural and Functional Aspects of the Normal Human Heart. Issue 4 (21st July 2016) - Caracausi, Maria; Piovesan, Allison; Vitale, Lorenza; Pelleri, Maria Chiara
Abstract : A systematic meta‐analysis of the available gene expression profiling datasets for the whole normal human heart generated a quantitative transcriptome reference map of this organ. Transcriptome Mapper (TRAM) software integrated 32 gene expression profile datasets from different sources returning a reference value of expression for each of the 43, 360 known, mapped transcripts assayed by any of the experimental platforms used in this regard. Main findings include the visualization at the gene and chromosomal levels of the classical description of the basic histology and physiology of the heart, the identification of suitable housekeeping reference genes, the analysis of stoichiometry of gene products, and the focusing on chromosome 21 genes, which are present in one excess copy in Down syndrome subjects, presenting cardiovascular defects in 30–40% of cases. Independent in vitro validation showed an excellent correlation coefficient (r = 0.98) with the in silico data. Remarkably, heart/non‐cardiac tissue expression ratio may also be used to anticipate that effects of mutations will most probably affect or not the heart. The quantitative reference global portrait of gene expression in the whole normal human heart illustrates the structural and functional aspects of the whole organ and is a general model to understand the mechanisms underlying heart pathophysiology. J. Cell. Physiol. 232: 759–770, 2017. © 2016 Wiley Periodicals, Inc. Abstract : A systematic meta‐analysis of the available gene expression profiling datasets for the whole normal human heart retrieved 32 microarray experiments performed on the whole organ. These data were integrated through the Transcriptome Mapper (TRAM) software to obtain a reference typical value of expression for each of the 43, 360 known, mapped transcripts. In vitro validation through real‐time RT‐PCR showed an excellent correlation coefficient (r = 0.98) with the in silico data.