Lethality and centrality in protein networks

Abstract

Lethality and centrality in protein networksCell biology traditionally identifies proteins based on their individual actions as catalysts, signaling molecules, or building blocks of cells and microorganisms. Currently, we witness the emergence of a post-genomic view that expands the protein's role, regarding it as an element in a network of proteinprotein interactions as well, with a 'contextual' or 'cellular' function within functional modules 1, 2 . Here we provide quantitative support for this paradigm shift by demonstrating that the phenotypic consequence of a single gene deletion in the yeast, S. cerevisiae, is affected, to a high degree, by the topologic position of its protein product in the complex, hierarchical web of molecular interactions.The S. cerevisiae protein-protein interaction network we investigate has 1870 proteins as nodes, connected by 2240 identified direct physical interactions, and is derived from combined, nonoverlapping data 3, 4 obtained mostly by systematic two-hybrid analyses 3 . Due to its size, a complete map of the network (Fig. 1a), while informative, in itself offers little insight into its large-scale characteristics. Thus, our first goal was to identify the architecture of this network, determining if it is best described by an inherently uniform exponential topology with proteins on average possessing the same number of links, or by a highly heterogeneous scale-free topology with proteins having widely different connectivities 5 . As we show in Fig. 1b, the probability that a given yeast protein interacts with k other yeast proteins follows a power-law 5 with an exponential cutoff 6 at k c ≅ 20, a topology that is also shared by the protein-protein interaction network of the bacterium, H. pylori 7 . This indicates that the network of protein interactions in two separate organisms forms a highly inhomogeneous scale-free network in which a few highly connected proteins play a central role in mediating interactions among numerous, less connected proteins.An important known consequence of the inhomogeneous structure is the network's simultaneous tolerance against random errors coupled with fragility against the removal of the most connected nodes 8 . Indeed, we find that random mutations in the genome of S. cerevisiae, -modeled by the removal of randomly selected yeast proteins-, do not affect the overall topology of the network. In contrast, when

Genes regulated by caloric restriction have unique roles within transcriptional networks

“…The network properties of CR-regulated genes may have implications regarding their evolutionary importance, as well as their involvement in central processes that underlie survival (Jeong et al, 2001;Carlson et al, 2006;Oldham et al, 2006). These implications, however, depend upon the interrelationships between genes, connectivity, network architecture and the phenotype, which are topics of ongoing investigation (Siegal et al, 2007).…”

“…These mechanisms may be linked to a conserved adaptive response that evolved to delay growth and conserve energy when resources are scarce (Holliday, 1989;Hart and Turturro, 1998;Kirkwood and Shanley, 2005), and genes underlying these processes may have key roles in transcriptional networks and survival in general. Previous studies have found that these properties, evolutionary conservation and essentiality, are characteristic of highlyconnected hubs within transcriptional and protein networks (Jeong et al, 2001;Bergmann et al, 2003;Carlson et al, 2006). This observation is consistent with the view that transcriptional networks evolve by adaptively adding new connections, building denser webs around conserved genes over evolutionary time.…”

A pooling-deconvolution strategy for biological network elucidation

“…Protein networks are best modeled as scale-free networks 25 , in which the majority of nodes have only a few neighbors while a small number of nodes ("hubs") have many. As expected, the coverage of interactions between proteins of high connectivity is lower than that between proteins of low connectivity (coverage for highly-connected nodes can be improved when the bit number is decreased, see Supplementary Fig.…”

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

“…Hence, most nodes have only few connections and few nodes have many connections. These highly connected nodes are called hubs (Albert et al ., 2000; Jeong et al ., 2001). Several other measures exist to describe the topology of networks and topological features of nodes.…”