A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein‐complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core‐attachment–based method named CO‐DPC to detect dynamic protein complexes is presented. First, CO‐DPC selects active proteins according to gene expression profiles and the 3‐sigma principle, and constructs dynamic PPI networks based on the co‐expression principle and PPI networks. Second, CO‐DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO‐DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.     

Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

Background

Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis.

Results

Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs.

Conclusion

A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction.

     

Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

Time-dependent network analysis reveals molecular targets underlying the development of diet-induced obesity and non-alcoholic steatohepatitis

Prolonged high-fat diet leads to the development of obesity and multiple comorbidities including non-alcoholic steatohepatitis (NASH), but the underlying molecular basis is not fully understood. We combine molecular networks and time course gene expression profiles to reveal the dynamic changes in molecular networks underlying diet-induced obesity and NASH. We also identify hub genes associated with the development of NASH. Core diet-induced obesity networks were constructed using Ingenuity pathway analysis (IPA) based on 332 high-fat diet responsive genes identified in liver by time course microarray analysis (8 time points over 24 weeks) of high-fat diet-fed mice compared to normal diet-fed mice. IPA identified five core diet-induced obesity networks with time-dependent gene expression changes in liver. These networks were associated with cell-to-cell signaling and interaction (Network 1), lipid metabolism (Network 2), hepatic system disease (Network 3 and 5), and inflammatory response (Network 4). When we merged these core diet-induced obesity networks, Tlr2, Cd14, and Ccnd1 emerged as hub genes associated with both liver steatosis and inflammation and were altered in a time-dependent manner. Further, protein–protein interaction network analysis revealed Tlr2, Cd14, and Ccnd1 were interrelated through the ErbB/insulin signaling pathway. Dynamic changes occur in molecular networks underlying diet-induced obesity. Tlr2, Cd14, and Ccnd1 appear to be hub genes integrating molecular interactions associated with the development of NASH. Therapeutics targeting hub genes and core diet-induced obesity networks may help ameliorate diet-induced obesity and NASH.     

Recognition pliability is coupled to structural heterogeneity: a calmodulin intrinsically disordered binding region complex

Protein interactions within regulatory networks should adapt in a spatiotemporal-dependent dynamic environment, in order to process and respond to diverse and versatile cellular signals. However, the principles governing recognition pliability in protein complexes are not well understood. We have investigated a region of the intrinsically disordered protein myelin basic protein (MBP(145-165)) that interacts with calmodulin, but that also promiscuously binds other biomolecules (membranes, modifying enzymes). To characterize this interaction, we implemented an NMR spectroscopic approach that calculates, for each conformation of the complex, the maximum occurrence based on recorded pseudocontact shifts and residual dipolar couplings. We found that the MBP(145-165)-calmodulin interaction is characterized by structural heterogeneity. Quantitative comparative analysis indicated that distinct conformational landscapes of structural heterogeneity are sampled for different calmodulin-target complexes. Such structural heterogeneity in protein complexes could potentially explain the way that transient and promiscuous protein interactions are optimized and tuned in complex regulatory networks.     

FoxO: linking new signaling pathways

Two recent reports reveal new roles for FoxO proteins in cell proliferation and tumorigenesis. Seoane and colleagues show that FoxO proteins play key roles in the TGFbeta-dependent activation of p21Cip1 by partnering with Smad3 and Smad4. FoxG1, a protein from a distinct Fox subfamily, binds FoxO/Smad complexes and blocks p21Cip1 expression. These interactions establish a relationship between the PI3K pathway, FoxG1, and the TGFbeta/Smad pathways. The second report identifies IkappaB kinase as a negative regulator of FoxO proteins, suggesting a mechanism for relieving negative regulation of cell cycle and promoting tumor cell proliferation.     

Ontology integration to identify protein complex in protein interaction networks

Background

Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.

Methods

We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.

Results

The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

     

XBP-1u suppresses autophagy by promoting the degradation of FoxO1 in cancer cells

Autophagy is activated to maintain cellular energy homeostasis in response to nutrient starvation. However, autophagy is not persistently activated, which is poorly understood at a mechanistic level. Here, we report that turnover of FoxO1 is involved in the dynamic autophagic process caused by glutamine starvation. X-box-binding protein-1u (XBP-1u) has a critical role in FoxO1 degradation by recruiting FoxO1 to the 20S proteasome. In addition, the phosphorylation of XBP-1u by extracellular regulated protein kinases1/2 (ERK1/2) on Ser61 and Ser176 was found to be critical for the increased interaction between XBP-1u and FoxO1 upon glutamine starvation. Furthermore, knockdown of XBP-1u caused the sustained level of FoxO1 and the persistent activation of autophagy, leading to a significant decrease in cell viability. Finally, the inverse correlation between XBP-1u and FoxO1 expression agrees well with the expression profiles observed in many human cancer tissues. Thus, our findings link the dynamic process of autophagy to XBP-1u-induced FoxO1 degradation.     

14-3-3 protein interacts with Huntingtin-associated protein 1 and regulates its trafficking

HAP1 (Huntingtin-associated protein 1) consists of two alternately spliced isoforms (HAP1A and HAP1B, which have unique C-terminal sequences) and participates in intracellular trafficking. The C terminus of HAP1A is phosphorylated, and this phosphorylation was found to decrease the association of HAP1A with kinesin light chain, a protein involved in anterograde transport in cells. It remains unclear how this phosphorylation functions to regulate the association of HAP1 with trafficking proteins. Using the yeast two-hybrid system, we found that HAP1 also interacts with 14-3-3 proteins, which are involved in the assembly of protein complexes and the regulation of protein trafficking. The interaction of HAP1 with 14-3-3 is confirmed by their immunoprecipitation and colocalization in mouse brain. Moreover, this interaction is specific to HAP1A and is increased by the phosphorylation of the C terminus of HAP1A. We also found that expression of 14-3-3 decreases the association of HAP1A with kinesin light chain. As a result, there is less HAP1A distributed in neurite tips of PC12 cells that overexpress 14-3-3. Also, overexpression of 14-3-3 reduces the effect of HAP1A in promoting neurite outgrowth of PC12 cells. We propose that the phosphorylation-dependent interaction of HAP1A with 14-3-3 regulates HAP1 function by influencing its association with kinesin light chain and trafficking in neuronal processes.     

Identifying protein complexes from interaction networks based on clique percolation and distance restriction

Background

Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks. Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.

Result

As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes.

Conclusion

The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance.

     

Methodology for protein-ligand binding studies: application to a model for drug resistance, the HIV/FIV protease system.

A protocol for the rapid energetic analysis of protein-ligand complexes has been developed. This protocol involves the generation of protein-ligand complex ensembles followed by an analysis of the binding free energy components. We apply this methodology toward understanding the origin of binding specificity within the human immunodeficiency virus/feline immunodeficiency virus (HIV/FIV) protease system, a model system for drug resistance studies. A distinct difference in the internal strain of an inhibitor within each protein environment clearly favors the HIV protease complex, as observed experimentally. Our analysis also predicts that residues within the S2-S3 pockets of the FIV protease active site are responsible for this strain. Close examination of the active site residue contributions to interaction energy and desolvation energy identifies specific amino acids that may also play a role in determining the binding preferences of these two enzymes. Proteins 1999;36:318-331.     

Protein interaction networks in bacteria

The complement of expressed cellular proteins - the proteome - is organized into functional, structured networks of protein interactions that mediate assembly of molecular machines and dynamic cellular pathways. Recent studies reveal the biological roles of protein interactions in bacteriophage T7 and Helicobacter pylori, and new methods allow to compare and to predict interaction networks in other species. Smaller scale networks provide biological insights into DNA replication and chromosome dynamics in Bacillus subtilis and Archeoglobus fulgidus, and into the assembly of multiprotein complexes such as the type IV secretion system of Agrobacterium tumefaciens, and the cell division machinery of Escherichia coli. Genome-wide interaction networks in several species are needed to obtain a biologically meaningful view of the higher order organization of the proteome in bacteria.     

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

Background

Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges.

Results

To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes.

Conclusions

We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes.