bioRxivBiochemistryPreprint
A Multi-omic and Multi-Species Analysis of Right Ventricular Failure
J. B. Mendelson, J. D. Sternbach, M. J. Doyle, L. Mills, L. M. Hartweck, W. Tollison, J. P. Carney, M. T. Lahti, R. W. Bianco, R. Kalra, F. Kazmirczak, C. Hindmarch, S. L. Archer, K. W. Prins, C. M. Martin
Abstract
Right ventricular failure (RVF) is a leading cause of morbidity and mortality in multiple cardiovascular diseases, but there are no approved treatments for RVF as therapeutic targets are not clearly defined. Contemporary transcriptomic/proteomic evaluations of RVF are predominately conducted in small animal studies, and data from large animal models are sparse. Moreover, a comparison of the molecular mediators of RVF across species is lacking. Here, we used transcriptomics and proteomics analyses to define the molecular pathways associated with cardiac MRI-derived values of RV hypertrophy, dilation, and dysfunction in pulmonary artery banded (PAB) piglets. Publicly available data from rat monocrotaline-induced RVF and pulmonary arterial hypertension patients with preserved or impaired RV function were used to compare the three species. Transcriptomic and proteomic analyses identified multiple pathways that were associated with RV dysfunction and remodeling in PAB pigs. Surprisingly, disruptions in fatty acid oxidation (FAO) and electron transport chain (ETC) proteins were different across the three species. FAO and ETC proteins and transcripts were mostly downregulated in rats, but were predominately upregulated in PAB pigs, which more closely matched the human data. Thus, the pig PAB metabolic molecular signature was more similar to human RVF than rodents. These data suggest there may be divergent molecular responses of RVF across species, and that pigs more accurately recapitulate the metabolic aspects of human RVF.
- organism: rat
- organism: human
- organism: plant (other)
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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