Three-dimensional reconstruction of protein networks provides insight into human genetic disease

Abstract

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.

Figure 1

Disease-associated proteins in the human structural interaction network (hSIN). (a) The procedure used to create the structural interaction network and to relate disease genes and mutations to this network. (b) Network representation of the main connected component of hSIN. Nodes represent proteins and edges correspond to structurally resolved interactions. Colored nodes indicate disease-associated proteins. The arrows point to the two main hubs: disease hub TP53 with 32 diseases and interaction hub GRB2 with 56 structurally resolved interactions. (c) Co-expression correlation of interacting proteins in the unfiltered interaction network, hPIN and hSIN. (d) Enrichment of functionally similar pairs in the unfiltered interaction network, hPIN and hSIN.

Figure 1

Disease-associated proteins in the human structural interaction network (hSIN). (a) The procedure used to create the structural interaction network and to relate disease genes and mutations to this network. (b) Network representation of the main connected component of hSIN. Nodes represent proteins and edges correspond to structurally resolved interactions. Colored nodes indicate disease-associated proteins. The arrows point to the two main hubs: disease hub TP53 with 32 diseases and interaction hub GRB2 with 56 structurally resolved interactions. (c) Co-expression correlation of interacting proteins in the unfiltered interaction network, hPIN and hSIN. (d) Enrichment of functionally similar pairs in the unfiltered interaction network, hPIN and hSIN.

Figure 2

Analysis of disease-associated mutations with respect to interaction interfaces. (a) Odds ratios for the distribution of in-frame mutations in different locations on proteins in hSIN. **P < 10⁻²⁰. P-values calculated using Z-tests for the log odds ratios. Error bars indicate ± SE. (b) Odds ratios for the distribution of truncating mutations in different locations on proteins in hSIN. (c) Odds ratios for the distribution of non-synonymous SNPs in different locations on proteins in hSIN. (d) Comparison of hSIN with mutations known to modify protein-protein interactions. (e) Illustration of MLH1 and PMS2 interaction interfaces. Colored stars indicate locations of experimentally tested in-frame mutations and SNPs. (f) Effects of in-frame mutations and SNPs on the MLH1-PMS2 interaction tested by Y2H. Flag tagged wild-type and mutant MLH1 were expressed in HEK293T cells, western blot analysis showed similar levels of MLH1 proteins. γ -tubulin was used as a loading control.

Figure 3

Analysis of pleiotropy and locus heterogeneity. (a) Fraction of mutation pairs on the same protein causing different diseases. **P < 10⁻²⁰. P-values calculated using binomial tests. (b) Illustration of WASP and its interaction interfaces with CDC42 and VASP. Colored stars indicate locations of experimentally tested mutations. (c) Effects on the WASP-CDC42 interaction by mutations on different interaction interfaces tested by Y2H. Flag tagged wild-type and mutant WASP were expressed in HEK293T cells, western blot analysis showed similar levels of WASP proteins. γ -tubulin was used as a loading control. (d) Fraction of mutation pairs on two proteins causing the same disease.

Figure 4

Modeling molecular mechanisms of disease genes and mutations through our structurally resolved interaction network. (a) Schematic illustration of using hSIN to understand complex genotype-to-phenotype relationships. In-frame mutations enriched on an interaction interface of protein X likely alter the interaction between protein X and A, leading to one disease, while mutations enriched on a different interface likely to alter the interaction between X and B, leading to another disease. Interactions between protein X and C, as well as X and D are likely to be intact under both scenarios. (b) Illustration of TP63 and its predicted interaction interface with TP73. Colored stars indicate locations of experimentally tested mutations. (c) Effects on the TP63-TP73 interaction by mutations on the predicted interacting interface tested by Y2H. Flag tagged wild-type and mutant TP63 were expressed in HEK293T cells, western blot analysis showed similar levels of TP63 proteins. γ -tubulin was used as a loading control.