Transcriptional regulation of protein complexes within and across species

Abstract

Yeast two-hybrid and coimmunoprecipitation experiments have defined large-scale protein-protein interaction networks for many model species. Separately, systematic chromatin immunoprecipitation experiments have enabled the assembly of large networks of transcriptional regulatory interactions. To investigate the functional interplay between these two interaction types, we combined both within a probabilistic framework that models the cell as a network of transcription factors regulating protein complexes. This framework identified 72 putative coregulated complexes in yeast and allowed the prediction of 120 previously uncharacterized transcriptional interactions. Several predictions were tested by new microarray profiles, yielding a confirmation rate (58%) comparable with that of direct immunoprecipitation experiments. Furthermore, we extended our framework to a cross-species setting, identifying 24 coregulated complexes that were conserved between yeast and fly. Analyses of these conserved complexes revealed different conservation levels of their regulators and provided suggestive evidence that protein-protein interaction networks may evolve more slowly than transcriptional interaction networks. Our results demonstrate how multiple molecular interaction types can be integrated toward a global wiring diagram of the cell, and they provide insights into the evolutionary dynamics of protein complex regulation. data integration ͉ network alignment ͉ network evolution T his decade has seen an enormous amount of data on molecular interactions released into the public domain. Although many types of molecules comprise the cell and can interact with one another, the two types that have been measured at largest scale are protein-protein interactions (PPIs) and transcriptional interactions (TIs). The two-hybrid system (1) and coimmunoprecipitation (co-IP) followed by mass spectrometry (2) have been the two most popular technologies to obtain large-scale PPI data. For transcriptional interactions, ChIP coupled with whole-genome DNA microarray (ChIP-chip) allow one to determine the entire spectrum of in vivo DNA-binding sites for any given protein (3, 4).The availability of large-scale PPI and TI data from multiple species has made it possible to study how these two interaction types are combined toward a coordinated cellular response. Previously, PPI and TI data have been integrated to infer hybrid network motifs (5), sets of interacting genes that are differentially expressed (6) and causal pathways that explain differential gene expression (7). In other recent studies (8, 9), yeast TIs were mapped onto known protein complexes as recorded in the Munich Information Center for Protein Sequences (MIPS) database (10). Although these studies did not consider PPI data directly, they established that proteins within the same complex are often encoded by genes that are regulated by the same transcription factors (TFs). Protein complexes were further shown to exhibit expression coherency (11) and to include synergistic TF pairs (12)....

Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks

“…For instance, among the functional categories enriched in transcriptional FFL clusters, we find mainly the core processes associated with transcription such as transcriptional control, DNA binding and regulation of metabolic processes (Supplementary Table S1), supporting the hypothesis that transcriptional FFLs play a universal information-processing role 24 . For the transcriptionally coregulated interacting proteins motif, it is usually assumed that enrichment and clustering reflects a 'regulonic complex' theme in which transcriptionally coregulated interacting proteins are often members of a protein complex 3,4,25 . We found that high-scoring coregulated protein clusters sometimes overlap with known protein complexes (Supplementary Table S2), but more often form 'functional protein networks' 26 ( Figure 2A(2)): subnetworks of the PPI network enriched for a particular function and identified by overlaying the protein interaction network with an additional layer of information, in this case regulator-target data.…”

The interactome: Predicting the protein-protein interactions in cells

“…Secondly, there are several types of possible interactions in living cells, from stable complexes up to temporary, functional pairings (for example, during the phosphorylation process in response to external stimuli). Protein complexes are better preserved during the evolution process than single proteins, so some computational methods focus on the prediction or searching of complexes that are common to several species [5][6][7][8][9][10][11][12]. Those methods use available information about experimentally verified interactions between proteins, orthologies, and comparison of protein sequences [13].…”

Automatic Parameter Learning for Multiple Local Network Alignment

“…As more experimental data gathers and network integration algorithms improve, network datasets with multiple data types will appear (Srinivasan et al, 2007), such as networks with interactions between proteins and DNA (Zhang et al, 2005;Tan et al, 2007), networks with physical as well as genetic interactions (Kelley and Ideker, 2005;Ulitsky et al, 2008), expression networks with boolean edges (Sahoo et al, 2007), and metabolic networks with chemical compounds (Kuhn et al, 2007). With future work to redefine Graemlin 2.0's feature functions, its scoring function and parameter learning algorithm will apply to these kinds of networks.…”