Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks
- Lina Chen†1Email author,
- Hong Wang†1,
- Liangcai Zhang†1,
- Wan Li1,
- Qian Wang1,
- Yukui Shang1,
- Yuehan He1,
- Weiming He2,
- Xu Li1,
- Jingxie Tai1 and
- Xia Li1Email author
© Chen et al; licensee BioMed Central Ltd. 2010
Received: 9 March 2010
Accepted: 22 July 2010
Published: 22 July 2010
Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.
Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.
Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.
One of key challenges of the post-genomic era is to understand the complexity of molecular networks, and describe their applications to elucidate essential principles of cellular systems and disease machinery [1, 2]. Spurred by advances in technology, several types of molecular networks, e.g. protein-protein interaction networks (PPINs), transcriptional regulatory networks (TRNs), and phenotype networks have been identified, providing us with a global landscape of how biological molecules may interact with one another. Many studies have demonstrated that PPINs and TRNs are essential for controlling the expression levels of genes and the activity of proteins, which mediates coordinated responses and adapted modifications to multifarious cellular stimuli [3, 4]. Given this landscape, integrative analysis of both PPINs and TRNs is a major focus in systems biology and bioinformatics. Many computational strategies based on integrated PPIN and TRN networks have been devised and used to decipher specific network structures [4, 5] or their potential biological implications  that underlie disease traits.
In molecular networks, genes, proteins, and other molecules form components called 'functional modules' that are densely interconnected, but relatively isolated from other networks . Recent surveys have shown that genes within a module or a cluster appear to have similar expression patterns, share common underlying regulatory mechanisms, and thus have strong associations with specific biological functions that determine the behaviour or phenotype of the cell [8, 9]. Complex diseases are known to result from the loss of one or more normal essential functions. One such example is cancer. In the recent years, an increasing number of cancer studies have combined human gene expression profiling and computational-based module searching algorithms to obtain a more comprehensive view of the molecular underpinnings and regulatory relationships of cancer . Segal et al.  have identified gene sets with similar behaviour across microarrays, and constructed 'cancer module maps' to characterize a variety of clinical conditions. Whitfield et al.  have detected modules in which genes shared both similar expression profiles and similar transcription factor binding profiles. Pomeroy et al.  have explored regulatory modules using the conservation of co-expression relationships across a diverse range of organisms. The utility of microarray analysis provides more interpretable results than using gene lists alone. A study by Chuang et al.  have combined microarray analysis and the human PPIN to identify sub-network biomarkers for breast cancer, and proposed that integrated network-based approaches could help researchers acquire additional and more accurate molecular mechanisms for cancers. Another study by Cui et al.  have demonstrated that the co-regulatory mechanism of molecular networks could mediate cancer-related genes, convey their abnormal states through several functional modules, and eventually lead to uncontrolled cell growth, invasion, and metastasis in distant planes of the body. Thus, uncovering co-regulated modular structures in integrated molecular networks could provide valuable insights into the pathogenesis of cancer.
In this paper, we introduce a probabilistic model termed Co-Regulatory Analysis using Integrated Networks (CRAIN) to detect human co-regulated modules using an integrative weighted network of a PPIN and a TRN. Then the performance of our analysis is evaluated by cross-validation with biological evidence. Furthermore, we figure out biological relevance of our modules for assembling or rewiring biological entities such as genes, protein complexes, and metabolic pathways. Finally, exemplified by cancer, we investigate whether co-regulated modules are capable of assembling different biological entities with underlying mechanisms in tumorigenesis.
Results and Discussion
Overview of the identification of co-regulated modules
We scaled and merged a human PPIN and TRN, and constructed a highly quality integrated network of protein and transcription regulation interactions. Adopting a probabilistic model, we evaluated whether a cluster of co-regulated proteins was likely to form a module in the integrated network. Under this model, we formulated a log-likelihood ratio to compare the fit of a cluster to the desired structure with its likelihood, given that the interaction map was randomly constructed. Highly scoring sub-networks corresponded to likely modules. We used a heuristic strategy for module-detecting procedures consisting of: (i) seed initialization; (ii) seed expanding; and (iii) overlap filtering. Finally, we obtained 96 co-regulated modules (Additional file 1), each of which was co-regulated by one or more specific transcription factors (TFs). And furthermore, we used three bipartite graphs to map our modules onto the biological entities of genes, protein complexes, and metabolic pathways to uncover the underlying biological significance of the modules. From our analysis, we concluded that in each module, co-regulated relationships might play important roles in packaging their binding genes, then extending to regulating complexes maintained by several physical interacting proteins, and thus involving in some metabolic pathways or disease traits.
Analysis of module robustness
Analysis of module functional coherency
Using the TANGO toolkit , we performed Gene Ontology (GO) enrichment analysis for our extracted 96 modules, to identify strongly-associated functional categories. The TANGO algorithm includes all levels of GO, and computes raw enrichment p-values using a standard hyper-geometric test with a significant level of p < 0.001. Annotation results showed that 77 modules (80%) were significantly enriched in biological function (Additional file 2).
Multiple methods comparison
Biological association of co-regulated modules with cancer
Cancer-related genes are often assumed to mediate each other through the co-regulatory mechanisms of molecular networks, causing abnormal states through several functional modules, and eventually leading to uncontrolled cell growth, invasion, and metastasis to distant planes of the body . To investigate this, we used Fisher's exact test to check biological associations between cancer-mutated genes and each of the 96 identified modules. We found that 42 (43%) of modules were associated with cancer (p < 0.05, Additional file 3).
Packaging features of co-regulated modules
Furthermore, to determine the biological importance of the co-regulated modules, we investigated the role of transcription regulation in assembling or rewiring genes, protein complexes, and metabolic pathways within modules.
To address whether genes that link to genes mutated in cancer in co-regulated modules are more likely to be cancer-associated, we interrogated non-mutated genes within modules associated with 'biopolymer metabolic process' (module 27), using manual literature validation. We found that all non-mutated genes were implicated in tumorigenesis (Additional file 4). These results suggested that genes in cancer-related co-regulated modules had a high disease risk for tumours, and might be tumour candidate biomarkers. Additional analysis found that similar results could be obtained for all other cancer related co-regulated modules (data not shown).
We devised and implemented a probabilistic model and a bipartite graph framework to infer human co-regulated modules. We analyzed their specific features in packaging different biological entities from an integrated molecular network with high confidence. Through robustness analysis, we demonstrated that our algorithm identified probable co-regulated modules for Homo sapiens. The performance of our approach was evaluated by comparison with other four module identification approaches. Further analysis using the bipartite graph framework uncovered packaging features for co-regulated modules, and showed that modules appeared to act as 'assemblers' dominated by several transcriptional regulations, and tended to coordinate complexes maintained by several physical interacting proteins, and indicating involvement in metabolic pathway cross-talk within neighbouring regions.
The success of our method can be attributed to the following factors. PPINs and TRNs are based on the curated literature and experimentally-determined interactions, so an integrated molecular network can be used to identify co-regulatory modules. In addition, we introduced a bipartite graph framework to evaluate packaging features of co-regulated modules with different biological entities, which easily divided biological entities into piles according to each module. As shown by various examples, our method appears to be effective in the identification of human co-regulated modules, and in searching for their packaging features in biological entities.
However, our proposed method has some limitations. We introduced a greedy algorithm aimed to make the locally optimal choice at each expanding step. Greedy algorithms are known to generally fail in finding globally optimal solutions, because they usually do not operate exhaustively on all the data. However, from our analysis results, we believe that the greedy algorithm was effective for module identification. The limitations of the proposed method for packaging (overlap) analysis are that two-thirds of human genes are annotated by at least one functional annotation, but the remaining one-third has yet to be annotated . In addition, the incompleteness of information about complexes and biological pathways might miss some significant overlaps or packing relationships. Although our proposed method has these limitations, the packaging features of co-regulated modules could still be deciphered in integrated molecular networks. With the accumulation of human data, we expect that our framework may facilitate the identification of additional modules and their packaging features.
Human interaction data sources
Human protein-protein interaction data was extracted from the HPRD databases (Release7) . The derived network contained 34,083 interactions between 9014 proteins. We determined edge reliability weights for these interactions with supporting evidence information including experimental validation, computational methods, and public literature mining for a number of proteins .
Transcriptional regulatory data was acquired from the Transfac Database (Release11.4) . The resulting regulatory network consisted of 281 TFs and 624 genes with 1603 interactions. For further analysis, we assigned an empirical weight to be 0.99 (a balanced confidence level of each edge in a TRN) for each transcriptional regulatory interaction.
Cancer mutated genes
Cancer mutated genes (384) were obtained from the Cancer Gene Census , a well-known online database cataloguing genes in which mutations have been causally implicated in a wide variety of tumour types.
Human co-regulated module identification
Integrative weighted network construction
The human integrated network was represented as a weighted graph. The vertices of the graph were proteins or TFs, and the edges were protein-protein interactions or transcription regulation interactions. All edges are set confidence scores, as described above.
Probabilistic statistical model
We constructed a probabilistic model to evaluate whether a cluster of co-regulated proteins is likely to form a module in an integrated network. An underlying assumption was that a module corresponds to a sub-network that is typically dense. Under the probabilistic model, we formulated a log-likelihood ratio used to compare the fit of our model of a module against the likelihood that it arose at random. Highly scoring sub-networks corresponded to likely modules.
Here, the ratio score of each candidate cluster is calculated by adding the log likelihood ratio score of the PPIN to that of the TRN. P (u, v) represents the confidence weight between two proteins u and v, and P (u, t) represents the confidence weight between protein u and transcription factor t. The probabilities R (u, v) and R (u, t) of the random network were estimated based on the percentage of the observed edge.
Each candidate cluster was generated from our searching algorithm. The searching process consisted of three basic processes: (i) seed initialization; (ii) seed expanding; and (iii) overlap filtering.
We defined candidate seeds as a set with a TF and two of its binding genes, and restricted to include two protein-protein interactions. A greedy approach was used to filter the candidate seeds, retaining those with the highest L-score as the staring seed subunits.
Where NO i is the size of the overlaps between any two modules, and NO u is the union size of any two modules. If the OR score of two modules was larger than 0.8, we merge the module with lower L-score into larger one.
Bridging co-regulated modules with biological entities using bipartite graphs
To access the packaging features of our resulting modules, we mapped them onto biological entities of genes, protein complexes, or metabolic pathways. For each module M, we constructed three 'Module-biological Entity' bipartite graphs: (i) GM-g = (M,g,EM-g) as a bipartite graph of module M-gene associations, where EM-g ⊆ M × g; (ii) GM-c = (M,c,EM-c) as a bipartite graph of module M-complex associations, where EM-c ⊆ M × c; and (iii) GM-p = (M,p,EM-p) as a bipartite graph of module M-pathway associations, where EM-p ⊆ M × p;. Finally, we collected the biological relevance of our modules for rewiring different biological entities. As exemplified by cancer, we investigated whether cancer related co-regulated modules could assemble different cancer-related biological entities, and identified underlying biological associations of our co-regulated modules with cancer.
This work was supported in part by the National Science Foundation of Heilongjiang Province [Grant Nos. D2007-48]; the National High Tech Development Project of China; the 863 Program (National High Technology Research and Development Program) [Grant Nos. 2007AA02Z329] and the Master Innovation Funds of Harbin Medical University [Grant Nos. HCXS2010006].
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