Stratus not altocumulus: a new view of the yeast protein interaction network

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.

Figure 1. Comparison of Network Topology

(A) Layout of FYI network [17]. (B) Layout of HC^fyi network. The small size and fragmented nature of FYI as compared to HC^fyi are evident: FYI contained 160 disconnected components with 57% in the main component, whereas HC^fyi contained 37 disconnected components with 93% in the main component. The global organization of FYI network is that of dense local regions that are sparsely interconnected (altocumulus structure), whereas that of the HC^fyi is densely interconnected overall, suggestive of extensive coordination and dependencies among diverse processes (stratus structure).

Figure 2. Global Degree Correlation Suggest Bias against Hub–Hub Interactions

Proteins in either a combined HTP dataset (A) or the full HC dataset (B) were binned logarithmically by connectivity (with two bins per decade), and interactions among nodes in each of these bins were computed. The ratios of observed over the expected number of interactions are shown; the x- and y-axes both represent connectivity. Degree correlation profiles were computed as described in the Methods section; expected values were computed by generating 50 random networks generated via edge-swapping procedure as described [16]. All colored regions are statistically significant (standard deviation greater than or equal to ±3, representing p < 0.01), with enrichment highlighted in blue and depletion in red; ratios colored in white do not deviate significantly from unity. Areas bounded by rectangles represent interaction space between hubs, defined as the degree threshold exceeded by 10% of the hubs. This threshold was 18 for HTP and 21 for HC.

Figure 3. Fraction of Hub–Hub Interactions Reduce with Scale of Experiments

Protein interactions from the LC dataset were separated into subgroups based on the number of interactions reported in the same publication, which was taken as a proxy for experimental scale. For each sub-network, hubs were defined as protein with connectivity exceeding the 90th percentile. The strong negative correlation (r = −0.85, p < 0.01, Spearman) indicates that as the size of the screen increases, bias against hub–hub interactions begins to appear.

Figure 4. Network Tolerance to Hub Deletion

The size of the largest component after deletion of hubs in the indicated networks was normalized by the initial size of the largest component. In all cases, hubs were deleted in descending order of connectivity. (A) The FYI network is sensitive to hub deletions, whereas the HC^fyi network is tolerant to hub deletions. (B) Topological sensitivity to node deletion can be modulated by varying the fraction of hub–hub interactions. Addition of 10% of random hub–hub interactions is sufficient to increase the tolerance of the FYI network to hub deletion (“augmented”). Deletion of 40% of interactions among hubs is necessary to increase the sensitivity of the HC^fyi network to hub deletion (“reduced”).

Figure 5. Date and Party Hubs Evolve at the Same Rate When Controlled for Protein Abundance

The relationship between dN (A) and dN/dS (B) as a function of protein abundance was determined for party hubs (open circles) and dates hubs (filled circles). Best-fit lines assuming equal slopes for the two hub types are shown (party hub, dashed line; date hub, solid line).

Figure 6. Modularity Paradigms

Two representations of protein interaction network topology are shown. (A) Conventional view of functional modularity in which hub-centric modules are sparsely interconnected, i.e., a modular physical topology underlies functional modules [16,17]. (B) An integrated view in which functional modules are heavily interconnected, as supported by the large-scale organization of dense HC networks.

Figure 7. More Highly Connected Proteins Exhibit Less Noise

Under both nutrient-poor (A) and nutrient-rich (B) conditions, more-connected proteins show less noise in their prevalence at the single cell level [43] when controlled for absolute protein abundance levels. For these plots, the data were split in to equal-sized bins of approximately equal connectivity. The values on the x-axis indicate the mean log connectivity of each bin.