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ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.

ABSTRACT: The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

SUBMITTER: Vlaic S

PROVIDER: S-EPMC5764996 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a "module" in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN) by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a "module". In a self-consistent manner, proteins are grouped into "functional roles" if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD) and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function prediction algorithms.

Project description:Complex scientific experiments provide researchers with a wealth of data and knowledge from heterogeneous sources. Analyzed in its entirety, OMICs data provide a deep insight into the overall biological processes of organisms. However, the integration of data from different cellular levels (e.g., transcriptomics and proteomics) is challenging. Analyzing lists of differentially abundant molecules from different cellular levels often results in a small overlap, which can be accounted to, e.g., different regulatory mechanisms, different temporal scales as well as inherent properties of the measurement method. Thus, there is a need for approaches that allow efficient integration of OMICs data from different cellular levels. In this study, we make use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall leading to a compensatory stress response. We analyze the experimental data based on two different approaches. First, we apply a simple approach based on the comparison of differentially regulated genes and proteins with subsequent pathway analysis. Second, we compare the cellular levels based on the identification of regulatory or functional modules by two module-detecting algorithms from protein-protein interaction networks in conjunction with transcriptomic and proteomic data. Our results show that both approaches associate the fungal caspofungin response with biological pathways like cell wall biosynthesis, fatty acid metabolism as well as carbohydrate metabolism. Compared to results of the simple approach, the use of regulatory modules shows a notably higher agreement between the different cellular levels. The additional structural information of the networks provided by the module-based approach allows for topological analysis as well as the analysis of the temporal evolution of cellular response at a molecular level. However, we also found that quality of the module-based results depends on the comprehensiveness of the underlying protein-protein interaction network itself. Thus, while our results highlight the benefits and potential provided by a module-based analysis of OMICs data from different cellular levels, future studies will have to focus on the expansion of organism specific protein-protein interaction networks.

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ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.

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