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Transverse Aortic Constriction in Mice
Published on: April 21, 2010
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Transverse aortic constriction multi-omics analysis uncovers pathophysiological cardiac molecular mechanisms
Enio Gjerga1,2,3, Matthias Dewenter3,4,5, Thiago Britto-Borges1,2,3
1Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, INF 669, Heidelberg 69120, Germany.
Summary
This study presents the TACOMA web application, integrating multi-omics data from a mouse model of heart failure (HF). TACOMA visualizes gene and protein changes over time, aiding the discovery of molecular mechanisms in cardiac remodeling and contractile dysfunction.
Area of Science:
- Cardiovascular Biology
- Molecular Biology
- Bioinformatics
Background:
- Progressive heart failure (HF) involves complex molecular changes in cardiac remodeling and function.
- Understanding these changes requires integrated multi-omics data over time.
Purpose of the Study:
- To present Transverse Aortic COnstriction Multi-omics Analysis (TACOMA), an interactive web application.
- To integrate and visualize transcriptomics and proteomics data from a murine model of HF.
Main Methods:
- Time-course transcriptomics (Illumina, Nanopore) and proteomics (mass spectrometry) in murine left and right ventricles post-transverse aortic constriction (TAC).
- Development of the TACOMA web application for data integration and visualization.
- Co-expression clustering and functional enrichment analysis.
Main Results:
- TACOMA visualizes gene/protein expression profiles, including alternative splicing events.
- Identified changes in metabolic genes/proteins during hypertrophic growth and contractile impairment.
- Highlighted novel transcripts and splicing changes (e.g., Tpm2 isoforms) implicated in HF pathogenesis.
Conclusions:
- TACOMA provides a valuable resource for exploring molecular events in heart failure.
- The integrated multi-omics data reveal key pathways in cardiac hypertrophy and dysfunction.
- Future work will expand data to diverse HF models to identify common and distinct molecular changes.
