Research
Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease
1 Rosetta Inpharmatics, LLC, Merck & Co., Inc., Terry Avenue North, Seattle, Washington 98109, USA
2 Department of Animal Science, University of Nebraska, Lincoln, NE 68508, USA
3 Department of Nutrition, Cell and Molecular Physiology, Carolina Center for Genome Science, University of North Carolina, Chapel Hill, NC 27599, USA
4 Current address: Pfizer Animal Health, Animal Genetics Business Unit, East 42nd Street, New York, NY 10017, USA
5 Current address: Pacific Biosciences, 1505 Adams Dr, Menlo Park, CA 94025, USA
Genome Biology 2009, 10:R55 doi:10.1186/gb-2009-10-5-r55
Published: 22 May 2009Abstract
Background
Obesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level.
Results
To provide an inter-tissue view of obesity with respect to molecular states that are associated with physiological states, we developed a framework for constructing tissue-to-tissue coexpression networks between genes in the hypothalamus, liver or adipose tissue. These networks have a scale-free architecture and are strikingly independent of gene-gene coexpression networks that are constructed from more standard analyses of single tissues. This is the first systematic effort to study inter-tissue relationships and highlights genes in the hypothalamus that act as information relays in the control of peripheral tissues in obese mice. The subnetworks identified as specific to tissue-to-tissue interactions are enriched in genes that have obesity-relevant biological functions such as circadian rhythm, energy balance, stress response, or immune response.
Conclusions
Tissue-to-tissue networks enable the identification of disease-specific genes that respond to changes induced by different tissues and they also provide unique details regarding candidate genes for obesity that are identified in genome-wide association studies. Identifying such genes from single tissue analyses would be difficult or impossible.
