Identifying Hubs in Protein Interaction Networks

In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs), it is common to define an ad hoc degree scale that defines "hub" proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust.In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction.We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks

A genome wide dosage suppressor network reveals genomic robustness

Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures

Identifying Hubs in Protein Interaction Networks

In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs), it is common to define an ad hoc degree scale that defines “hub” proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust.In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction.We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks.</p

Statistical significance of relative subgraph connectivity.

<p>Empirical P-values (dashed lines) for significance of the relative connectivity measure (solid lines) for all the four networks were computed using 10,000 random networks corresponding to each real network. P-values that are less than 10⁻⁴ can be identified by the circles on the x-axis in each panel.</p

Dip statistics as a function of number of included hubs.

<p>Values of the dip statistic for all four networks studied as a function of the number of top degree nodes included in the hub set. The straight line marks the boundary between statistically significant and insignificant dip values (at 95% confidence).</p

Bimodality of PCC distribution for the HC network.

<p>Inclusion of non-hub nodes into the list of HC hubs leads to reduction in bi-modality of the average PCC distribution. This can be seen as the number of hubs included increases from 40 to 419 in the HC dataset. The panel on the left displays smoothed probability density functions corresponding to the average PCC distribution while the panel on the right displays the cumulative distribution functions. Percentiles refer to the percentages of top high degree nodes included in the hub set, following <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005344#pone.0005344-Batada1" target="_blank">[19]</a>.</p

A cartoon illustrating relative connectivity of subgraphs.

<p>Successive subgraphs are generated from a ranked degree list, and the relative connectivity <i>f</i> is computed from them. Each node is represented by a black center with a gray ‘halo’ whose size is proportional to the degree of the node. Note that newer nodes have smaller halos (lower degrees). Interactions involving newly added nodes are shown as dotted edges, while previously established interactions are shown as dark edges. Note that all subgraphs upto <i>G₄</i> are completely disconnected in this example.</p

Robustness of relative subgraph connectivity.

<p>Relative subgraph connectivity profiles for unperturbed versions of all four networks are shown, along with the corresponding profiles upon random addition and removal of 10% and 15% of the edges in the unperturbed networks.</p

Essential gene enrichment.

<p>Enrichment for essential genes among hubs relative to non-hubs, as measured by the Jensen-Shannon divergence (upper panels) and the P-value for the Kolmogorov-Smirnov test (lower panels).</p

Comparison between degree cutoffs for defining hubs by our relative connectivity based method and definitions used in the literature.

<p>Comparison between degree cutoffs for defining hubs by our relative connectivity based method and definitions used in the literature.</p