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Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network.

Keith BP, Robertson DL, Hentges KE - Front Genet (2014)

Bottom Line: Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population.Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity.We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Life Sciences, University of Manchester Manchester, UK.

ABSTRACT
Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

No MeSH data available.


Related in: MedlinePlus

Interconnectivity of Bardet–Biedl syndrome genes. Circular nodes represent proteins, with the lines between them signifying an interaction between the two proteins.
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Figure 5: Interconnectivity of Bardet–Biedl syndrome genes. Circular nodes represent proteins, with the lines between them signifying an interaction between the two proteins.

Mentions: Clustering analysis of our network using the MCODE algorithm (Bader and Hogue, 2003) revealed a high scoring cluster, in which many nodes were tagged as BBS affected proteins (Figure 5). This module shows a number of locus heterogeneity genes (red), all of which encode BBS causing proteins. Surrounding nodes for genes not currently associated with BBS (gray) interconnect with a minimum of two BBS causing genes, suggesting a potential involvement in or cause of BBS for these other connected genes.


Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network.

Keith BP, Robertson DL, Hentges KE - Front Genet (2014)

Interconnectivity of Bardet–Biedl syndrome genes. Circular nodes represent proteins, with the lines between them signifying an interaction between the two proteins.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4260505&req=5

Figure 5: Interconnectivity of Bardet–Biedl syndrome genes. Circular nodes represent proteins, with the lines between them signifying an interaction between the two proteins.
Mentions: Clustering analysis of our network using the MCODE algorithm (Bader and Hogue, 2003) revealed a high scoring cluster, in which many nodes were tagged as BBS affected proteins (Figure 5). This module shows a number of locus heterogeneity genes (red), all of which encode BBS causing proteins. Surrounding nodes for genes not currently associated with BBS (gray) interconnect with a minimum of two BBS causing genes, suggesting a potential involvement in or cause of BBS for these other connected genes.

Bottom Line: Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population.Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity.We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Life Sciences, University of Manchester Manchester, UK.

ABSTRACT
Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

No MeSH data available.


Related in: MedlinePlus