Abstract

Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: “party” hubs are co-expressed and co-localized with their partners, whereas “date” hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.

Highlights

  • One of the advantages of a systems biological approach to understanding the relationship between genomes and phenotypes is that it permits conceptualization of intermediary levels of organization, which may not be evident from morefocused studies [1,2,3]

  • Network Layout Suggests Different Global Organization To allow direct comparison to the filtered yeast interactome (FYI) network, we used a framework of all FYI proteins, to which interactions were added from the HC dataset to yield the HCfyi dataset

  • The most striking difference between the two networks was the suppression of connectivity among hubs in FYI; this result is encapsulated in the degree anticorrelation pattern observed previously for a HTP network [16]

Read more

Summary

Introduction

One of the advantages of a systems biological approach to understanding the relationship between genomes and phenotypes is that it permits conceptualization of intermediary levels of organization, which may not be evident from morefocused studies [1,2,3]. Some of these levels can be objectively defined (e.g., cliques [4,5,6] and motifs [7,8]), others attempt to capture novel levels of organization in a more subjective fashion. That modules can be treated as an entity might be defended on the basis that the proteins in the same module have a similar knockout phenotype (i.e., if one is essential, all are essential; if one is nonessential, all might be nonessential), that they are phylogenetically correlated (i.e., if one is present in a given species, all are present, and vice versa) [15] or, possibly, on the basis that the constituent proteins are co-expressed

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call