The relationship between genetic mutations and human diseases is often complex and ambiguous: a given disease can be associated with mutations in distinct genes and, conversely, mutations in a given gene can be associated with several diseases. Can this many-to-many relationship be exploited to construct a human disease network and extract information on the human disease landscape?
In their work just published in PNAS, Albert-László Barabasi, Marc Vidal and colleagues reconstruct such a “diseasome” network in which disorders are linked to the respective associated disease genes (Goh et al, 2007 PNAS). Two projections of the network are presented: a) the Human Disease Network (HDN), in which diseases are connected to each other if they share a common disease gene; b) the Disease Gene Network (DGN), in which genes are connected if they are associated with a common disease. The HDN has a giant component comprising almost half of the diseases, in which some classes of disorders cluster naturally (eg cancers or neurological disorders, but not metabolic disorders). The DGN, when integrated with functional annotations, expression and protein-protein interaction data, provides a first step towards a “network-based explanation of the emergence of complex polygenic disorders” in the sense that it reveals, perhaps not too surprisingly, how functionally related genes can lead to similar disorders.
The authors also look at the centrality of human disease genes in the protein-protein interaction network. An interesting twist comes when human disease genes are separated into essential and non-essential classes, according to the lethal or non lethal mouse phenotype resulting from the knockout of the respective orthologous genes. While essential genes tend to be associated with hubs in the interactome, disease genes that are non-essential (representing 78% of all disease genes) do not display a higher connectivity than non-disease genes. A somewhat complementary conclusion was recently reached by Lu and colleagues when looking at changes in gene expression in a mouse model of asthma: genes whose expression is the most affected by the disease have low connectivity while genes coding for hub proteins tend to display stable expression levels (Lu et al, 2007 Mol Syst Biol 3:98).
Reading this work, two main questions come to my mind:
First, if a majority of disease genes are not more central than non-disease genes, what will be the “network-based explanation” for the mere fact that they are implicated in a human disease? What kind of model will be needed to achieve this fundamental prediction?
Second and on a more general note, it looks to me that system-level approaches will be needed to integrate the environmental causes to human disease. While there is no question about the power of genetics and genomics to provide a global view on human diseases, I find it useful to remember that, as Jeremy Nicholson emphasizes,
the majority of people in the world die from what are, in the broadest sense, environmental causes. (Nicholson 2007, Mol Syst Biol 2:52)
Concrete achievements of Systems Biology in addressing significant human health problems may well require strong research efforts to bring system-level understanding into the impact of environmental factors on disease. This way, the Human Genetic Disease Network might ultimately be extended to a true Human
Genetic Disease Network.