Genome-scale analysis of interaction dynamics reveals organization of biological networks

Abstract

Analyzing large-scale interaction networks has generated numerous insights in systems biology. However, such studies have primarily been focused on highly co-expressed, stable interactions. Most transient interactions that carry out equally important functions, especially in signal transduction pathways, are yet to be elucidated and are often wrongly discarded as false positives. Here, we revisit a previously described Smith-Waterman-like dynamic programming algorithm and use it to distinguish stable and transient interactions on a genomic scale in human and yeast. We find that in biological networks, transient interactions are key links topologically connecting tightly regulated functional modules formed by stable interactions and are essential to maintaining the integrity of cellular networks. We also perform a systematic analysis of interaction dynamics across different technologies and find that high-throughput yeast two-hybrid is the only available technology for detecting transient interactions on a large scale.

Fig. 1.

Cartoon depiction of protein–protein interaction dynamics. (A) Gene expression profiles for two proteins that are highly correlated under all conditions indicating a stable or globally co-expressed interaction. (B) Two contiguous blocks of significant co-expression indicate this pair of proteins is transiently interacting or locally co-expressed.

Fig. 2.

(A, B) Enrichment of PCC of co-expression of interacting proteins (detected by different technologies) as opposed to random gene pairs in human and yeast respectively. (C, D) Comparison of transient interactions detected per technology in human and yeast, respectively. The dashed line indicates the overall average detection of transient interactions.

Fig. 3.

(A) The expression profiles of SFB2 and SEC23 (co-expression only in the final yellow block). (B, C) Transient interactions in human are enriched in “date hubs”. These have previously been shown to be vital in forming important topological links between stable functional modules. (D) Transient interactions in human and yeast have a significantly higher betweenness value–they hold the key in maintaining the integrity of cellular networks. (E, F) Characteristic path length as a measure of network connectivity after successive removal of edges of the network. Each data point represents the removal of a fixed percentage of overall nodes of the graph from each interaction type. Random removal occurs on all interactions in the network, which may include other interactions that are still uncategorized as transient or stable. Removal of transient interactions increases path length more sharply than disturbing random or stable interactions.