Open Access Highly Accessed Open Badges Research article

Genome-wide expression patterns in physiological cardiac hypertrophy

Ignat Drozdov12, Sophia Tsoka2, Christos A Ouzounis23 and Ajay M Shah1*

Author Affiliations

1 King's College London (KCL) BHF Centre of Research Excellence - Cardiovascular Division - School of Medicine - James Black Centre - 125 Coldharbour Lane, London SE5 9NU - UK

2 Centre for Bioinformatics - School of Physical Sciences & Engineering - King's College London (KCL) - Strand, London WC2R 2LS - UK

3 Computational Genomics Unit, Institute of Agrobiotechnology - Centre for Research & Technology Hellas - PO Box 361, GR-57001 Thessaloniki - Greece

For all author emails, please log on.

BMC Genomics 2010, 11:557  doi:10.1186/1471-2164-11-557

Published: 11 October 2010



Physiological left ventricular hypertrophy (LVH) involves complex cardiac remodeling that occurs as an adaptive response to chronic exercise. A stark clinical contrast exists between physiological LVH and pathological cardiac remodeling in response to diseases such as hypertension, but little is known about the precise molecular mechanisms driving physiological adaptation.


In this study, the first large-scale analysis of publicly available genome-wide expression data of several in vivo murine models of physiological LVH was carried out using network analysis. On evaluating 3 million gene co-expression patterns across 141 relevant microarray experiments, it was found that physiological adaptation is an evolutionarily conserved processes involving preservation of the function of cytochrome c oxidase, induction of autophagy compatible with cell survival, and coordinated regulation of angiogenesis.


This analysis not only identifies known biological pathways involved in physiological LVH, but also offers novel insights into the molecular basis of this phenotype by identifying key networks of co-expressed genes, as well as their topological and functional properties, using relevant high-quality microarray experiments and network inference.