Last month, Wang et al. published in Nature Biotechnology an interesting study on computational network, protein structure modelling and diseases associated. The concept is a brillant illustration of how associating multiple genomics dataset into one:
- They took all information about protein-protein interactions.
- Using these two datasets, they produced a high quality structurally resolved network (hSIN).
- They took genes involved in diseases.
- Finally, they mapped the interacting sites and diseases mutation onto the corresponding structures.
Different conclusions emerged (or confirmed previous results) from this study:
- Non-synonymous SNPs in proteins involved in disease are randomly distributed, menaing that most SNP are non-disease related.
- Genes can be involved in multiple unrelated diseases (pleiotopic effect). They could have mutations at one interface leading to one particular disease as well as they could have mutation to the opposite side (see Figure) or to another domain, so leading to another disease. One example illustrated is the WASP gene. WASP can interact with CDC42 and VASP. When WASP is mutated (in WH1 domain) to prevent binding with CDC42, it leads to X-linked neutropenia (XLN). When WASP is mutated (in PBD domain) to prevent binding with Wiskott-Aldrich (WAS) and/or X-linked thrombocytopenia (XLT).
- Using their hSIN dataset, they predicted around 300 candidate genes for 700 unknow disease-to-gene associations.
|Pleiotropic effect. Mutation 1 in protein A will lead to disease 1 by blocking binding to protein B. Mutation 2 in protein A will lead to disease 2 by blocking binding to protein C. (Figure inspired from original publication)|
Wang X, Wei X, Thijssen B, Das J, Lipkin SM, Yu H.
Nat Biotechnol. 2012 Jan 15;30(2):159-64. doi: 10.1038/nbt.2106.