Identifying responsive functional modules from protein-protein interaction network

Mechanism of tanshinones and phenolic acids from Danshen in the treatment of coronary heart disease based on co-expression network

Background

The tanshinones and phenolic acids in Salvia miltiorrhiza (also named Danshen) have been confirmed for the treatment of coronary heart disease (CHD), but the action mechanisms remain elusive.

Methods

In the current study, the co-expression protein interaction network (Ce-PIN) was used to illustrate the differences between the tanshinones and phenolic acids of Danshen in the treatment of CHD. By integrating the gene expression profile data and protein-protein interactions (PPIs) data, the Ce-PINs of tanshinones and phenolic acids were constructed. Then, the Ce-PINs were analyzed by gene ontology enrichment analyzed based on the optimal algorithm.

Results

It turned out that Danshen is able to treat CHD by regulating the blood circulation, immune response and lipid metabolism. However, phenolic acids may regulate the blood circulation by Extracellular calcium-sensing receptor (CaSR), Endothelin-1 receptor (EDNRA), Endothelin-1 receptor (EDNRB), Kininogen-1 (KNG1), tanshinones may regulate the blood circulation by Guanylate cyclase soluble subunit alpha-1 (GUCY1A3) and Guanylate cyclase soluble subunit beta-1 (GUCY1B3). In addition, both the phenolic acids and tanshinones may regulate the immune response or inflammation by T-cell surface glycoprotein CD4 (CD4), Receptor-type tyrosine-protein phosphatase C (PTPRC).

Conclusion

Through the same targets of the same biological process and different targets of the same biological process, the tanshinones and phenolic acids synergistically treat coronary heart disease.

XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting

Hot spot residues at protein⁻RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein⁻RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein⁻RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein⁻RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots.

Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces.

An efficient method for protein function annotation based on multilayer protein networks

Background

Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict the function of proteins. However, the precision of these predictions still needs to be improved, due to the incompletion and noise in PPI networks. Integrating network topology and biological information could improve the accuracy of protein function prediction and may also lead to the discovery of multiple interaction types between proteins. Current algorithms generate a single network, which is archived using a weighted sum of all types of protein interactions.

Method

The influences of different types of interactions on the prediction of protein functions are not the same. To address this, we construct multilayer protein networks (MPN) by integrating PPI networks, the domain of proteins, and information on protein complexes. In the MPN, there is more than one type of connections between pairwise proteins. Different types of connections reflect different roles and importance in protein function prediction. Based on the MPN, we propose a new protein function prediction method, named function prediction based on multilayer protein networks (FP-MPN). Given an un-annotated protein, the FP-MPN method visits each layer of the MPN in turn and generates a set of candidate neighbors with known functions. A set of predicted functions for the testing protein is then formed and all of these functions are scored and sorted. Each layer plays different importance on the prediction of protein functions. A number of top-ranking functions are selected to annotate the unknown protein.

Conclusions

The method proposed in this paper was a better predictor when used on Saccharomyces cerevisiae protein data than other function prediction methods previously used. The proposed FP-MPN method takes different roles of connections in protein function prediction into account to reduce the artificial noise by introducing biological information.

Electronic supplementary material

The online version of this article (doi:10.1186/s40246-016-0087-x) contains supplementary material, which is available to authorized users.

Mutual Interactions between Aquaporins and Membrane Components

In recent years, a number of studies have been focused on the structural evaluation of protein complexes in order to get mechanistic insights into how proteins communicate at the molecular level within the cell. Specific sites of protein-aquaporin interaction have been evaluated and new forms of regulation of aquaporins described, based on these associations. Heterotetramerizations of aquaporin isoforms are considered as novel regulatory mechanisms for plasma membrane (PIPs) and tonoplast (TIPs) proteins, influencing their intrinsic permeability and trafficking dynamics in the adaptive response to changing environmental conditions. However, protein-protein interaction is an extensive theme that is difficult to tackle and new methodologies are being used to study the physical interactions involved. Bimolecular fluorescence complementation and the identification of cross-linked peptides based on tandem mass spectra, that are complementary to other methodologies such as heterologous expression, co-precipitation assays or confocal fluorescence microscopy, are discussed in this review. The chemical composition and the physical characteristics of the lipid bilayer also influence many aspects of membrane aquaporins, including their functionality. The molecular driving forces stabilizing the positions of the lipids around aquaporins could define their activity, thereby altering the conformational properties. Therefore, an integrative approach to the relevance of the membrane-aquaporin interaction to different processes related to plant cell physiology is provided. Finally, it is described how the interactions between aquaporins and copolymer matrixes or biological compounds offer an opportunity for the functional incorporation of aquaporins into new biotechnological advances.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution.

Network biology has focused for years on protein-protein interaction (PPI) networks, identifying nodes with central structural functions and modules associated to bioprocesses, phenotypes and diseases. Network biology field moved to a higher level of abstraction, and started characterizing a less intuitive kind of interactions, called genetic interactions (GIs) or epistasis. Mostly due to technical challenges associated to the genome-wide mapping of GIs, these studies primarily focused on unicellular organisms. They uncovered modules embedded within the structure of these networks and started characterizing their relationship with PPI-network and biological functions. We provide here the first in-depth characterization of a network composed of ~600 GIs within signaling and metabolic pathways of a multicellular organism, the nematode Caenorhabditis elegans. We characterize the structure of this network, and the function of GI classes found in this network. We also discuss how these GI classes contribute to the genomic robustness and the adaptive evolution of multicellular organisms.

Identification of Novel Pathways in Plant Lectin-Induced Cancer Cell Apoptosis

Plant lectins have been investigated to elucidate their complicated mechanisms due to their remarkable anticancer activities. Although plant lectins seems promising as a potential anticancer agent for further preclinical and clinical uses, further research is still urgently needed and should include more focus on molecular mechanisms. Herein, a Naïve Bayesian model was developed to predict the protein-protein interaction (PPI), and thus construct the global human PPI network. Moreover, multiple sources of biological data, such as smallest shared biological process (SSBP), domain-domain interaction (DDI), gene co-expression profiles and cross-species interolog mapping were integrated to build the core apoptotic PPI network. In addition, we further modified it into a plant lectin-induced apoptotic cell death context. Then, we identified 22 apoptotic hub proteins in mesothelioma cells according to their different microarray expressions. Subsequently, we used combinational methods to predict microRNAs (miRNAs) which could negatively regulate the abovementioned hub proteins. Together, we demonstrated the ability of our Naïve Bayesian model-based network for identifying novel plant lectin-treated cancer cell apoptotic pathways. These findings may provide new clues concerning plant lectins as potential apoptotic inducers for cancer drug discovery.

Quantitative assessment of gene expression network module-validation methods

Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.

ModuleBlast: identifying activated sub-networks within and across species

Identifying conserved and divergent response patterns in gene networks is becoming increasingly important. A common approach is integrating expression information with gene association networks in order to find groups of connected genes that are activated or repressed. In many cases, researchers are also interested in comparisons across species (or conditions). Finding an active sub-network is a hard problem and applying it across species requires further considerations (e.g. orthology information, expression data and networks from different sources). To address these challenges we devised ModuleBlast, which uses both expression and network topology to search for highly relevant sub-networks. We have applied ModuleBlast to expression and interaction data from mouse, macaque and human to study immune response and aging. The immune response analysis identified several relevant modules, consistent with recent findings on apoptosis and NFκB activation following infection. Temporal analysis of these data revealed cascades of modules that are dynamically activated within and across species. We have experimentally validated some of the novel hypotheses resulting from the analysis of the ModuleBlast results leading to new insights into the mechanisms used by a key mammalian aging protein.

Gene module based regulator inference identifying miR-139 as a tumor suppressor in colorectal cancer

Colorectal cancer is one of the most commonly diagnosed cancer types worldwide. Identification of the key regulators of the altered biological networks is crucial for understanding the complex molecular mechanisms of colorectal cancer. We proposed a gene module based approach to infer key miRNAs regulating the major gene network alterations in cancer tissues. By integrating gene differential expression and co-expression information with a protein-protein interaction network, the differential gene expression modules, which captured the major gene network changes, were identified for colorectal cancer. Then, several key miRNAs, which extensively regulate the gene modules, were inferred by analyzing their target gene enrichment in the modules. Among the inferred candidates, three miRNAs, miR-101, miR-124 and miR-139, are frequently down-regulated in colorectal cancers. The following computational and experimental analyses demonstrate that miR-139 can inhibit cell proliferation and cell cycle G1/S transition. A known oncogene ETS1, a key transcription factor in the gene module, was experimentally verified as a novel target of miR-139. miR-139 was found to be significantly down-regulated in early pathological cancer stages and its expression remained at very low levels in advanced stages. These results indicate that miR-139, inferred by the gene module based approach, should be a key tumor suppressor in early cancer development.

Spatiotemporal positioning of multipotent modules in diverse biological networks

A biological network exhibits a modular organization. The modular structure dependent on functional module is of great significance in understanding the organization and dynamics of network functions. A huge variety of module identification methods as well as approaches to analyze modularity and dynamics of the inter- and intra-module interactions have emerged recently, but they are facing unexpected challenges in further practical applications. Here, we discuss recent progress in understanding how such a modular network can be deconstructed spatiotemporally. We focus particularly on elucidating how various deciphering mechanisms operate to ensure precise module identification and assembly. In this case, a system-level understanding of the entire mechanism of module construction is within reach, with important implications for reasonable perspectives in both constructing a modular analysis framework and deconstructing different modular hierarchical structures.

Exploration of potential roles of a new LOXL2 splicing variant using network knowledge in esophageal squamous cell carcinoma

LOXL2 (lysyl oxidase-like 2), an enzyme that catalyzes oxidative deamination of lysine residue, is upregulated in esophageal squamous cell carcinoma (ESCC). A LOXL2 splice variant LOXL2-e13 and its wild type were overexpressed in ESCC cells followed by microarray analyses. In this study, we explored the potential role and molecular mechanism of LOXL2-e13 based on known protein-protein interactions (PPIs), following microarray analysis of KYSE150 ESCC cells overexpressing a LOXL2 splice variant, denoted by LOXL2-e13, or its wild-type counterpart. The differentially expressed genes (DEGs) of LOXL2-WT and LOXL2-e13 were applied to generate individual PPI subnetworks in which hundreds of DEGs interacted with thousands of other proteins. These two DEG groups were annotated by Functional Annotation Chart analysis in the DAVID bioinformatics database and compared. These results found many specific annotations indicating the potential specific role or mechanism for LOXL2-e13. The DEGs of LOXL2-e13, comparing to its wild type, were prioritized by the Random Walk with Restart algorithm. Several tumor-related genes such as ERO1L, ITGA3, and MAPK8 were found closest to LOXL2-e13. These results provide helpful information for subsequent experimental identification of the specific biological roles and molecular mechanisms of LOXL2-e13. Our study also provides a work flow to identify potential roles of splice variants with large scale data.

Integrative approaches for finding modular structure in biological networks

A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

Tamoxifen integromics and personalized medicine: dynamic modular transformations underpinning response to tamoxifen in breast cancer treatment

Recent advances in pharmacogenomics technologies allow bold steps to be taken towards personalized medicine, more accurate health planning, and personalized drug development. In this framework, systems pharmacology network-based approaches offer an appealing way for integrating multi-omics data and set the basis for defining systems-level drug response biomarkers. On the road to individualized tamoxifen treatment in estrogen receptor-positive breast cancer patients, we examine the dynamics of the attendant pharmacological response mechanisms. By means of an "integromics" network approach, we assessed the tamoxifen effect through the way the high-order organization of interactome (i.e., the modules) is perturbed. To accomplish that, first we integrated the time series transcriptome data with the human protein interaction data, and second, an efficient module-detecting algorithm was applied onto the composite graphs. Our findings show that tamoxifen induces severe modular transformations on specific areas of the interactome. Our modular biomarkers in response to tamoxifen attest to the immunomodulatory role of tamoxifen, and further reveal that it deregulates cell cycle and apoptosis pathways, while coordinating the proteasome and basal transcription factors. To the best of our knowledge, this is the first report that informs the fields of personalized medicine and clinical pharmacology about the actual dynamic interactome response to tamoxifen administration.

Linking Interactome to Disease

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

Network-based drug repositioning

Network-based computational biology, with the emphasis on biomolecular interactions and omics-data integration, has had success in drug development and created new directions such as drug repositioning and drug combination. Drug repositioning, i.e., revealing a drug's new roles, is increasingly attracting much attention from the pharmaceutical community to tackle the problems of high failure rate and long-term development in drug discovery. While drug combination or drug cocktails, i.e., combining multiple drugs against diseases, mainly aims to alleviate the problems of the recurrent emergence of drug resistance and also reveal their synergistic effects. In this paper, we unify the two topics to reveal new roles of drug interactions from a network perspective by treating drug combination as another form of drug repositioning. In particular, first, we emphasize that rationally repositioning drugs in the large scale is driven by the accumulation of various high-throughput genome-wide data. These data can be utilized to capture the interplay among targets and biological molecules, uncover the resulting network structures, and further bridge molecular profiles and phenotypes. This motivates many network-based computational methods on these topics. Second, we organize these existing methods into two categories, i.e., single drug repositioning and drug combination, and further depict their main features by three data sources. Finally, we discuss the merits and shortcomings of these methods and pinpoint some future topics in this promising field.

A semi-supervised boosting SVM for predicting hot spots at protein-protein interfaces

BACKGROUND

Hot spots are residues contributing the most of binding free energy yet accounting for a small portion of a protein interface. Experimental approaches to identify hot spots such as alanine scanning mutagenesis are expensive and time-consuming, while computational methods are emerging as effective alternatives to experimental approaches.

RESULTS

In this study, we propose a semi-supervised boosting SVM, which is called sbSVM, to computationally predict hot spots at protein-protein interfaces by combining protein sequence and structure features. Here, feature selection is performed using random forests to avoid over-fitting. Due to the deficiency of positive samples, our approach samples useful unlabeled data iteratively to boost the performance of hot spots prediction. The performance evaluation of our method is carried out on a dataset generated from the ASEdb database for cross-validation and a dataset from the BID database for independent test. Furthermore, a balanced dataset with similar amounts of hot spots and non-hot spots (65 and 66 respectively) derived from the first training dataset is used to further validate our method. All results show that our method yields good sensitivity, accuracy and F1 score comparing with the existing methods.

CONCLUSION

Our method boosts prediction performance of hot spots by using unlabeled data to overcome the deficiency of available training data. Experimental results show that our approach is more effective than the traditional supervised algorithms and major existing hot spot prediction methods.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Sensitive detection of pathway perturbations in cancers

Background

The normal functioning of a living cell is characterized by complex interaction networks involving many different types of molecules. Associations detected between diseases and perturbations in well-defined pathways within such interaction networks have the potential to illuminate the molecular mechanisms underlying disease progression and response to treatment.

Results

In this paper, we present a computational method that compares expression profiles of genes in cancer samples to samples from normal tissues in order to detect perturbations of pre-defined pathways in the cancer. In contrast to many previous methods, our scoring function approach explicitly takes into account the interactions between the gene products in a pathway. Moreover, we compute the sub-pathway that has the highest score, as opposed to merely computing the score for the entire pathway. We use a permutation test to assess the statistical significance of the most perturbed sub-pathway. We apply our method to 20 pathways in the Netpath database and to the Global Cancer Map of gene expression in 18 cancers. We demonstrate that our method yields more sensitive results than alternatives that do not consider interactions or measure the perturbation of a pathway as a whole. We perform a sensitivity analysis to show that our approach is robust to modest changes in the input data. Our method confirms numerous well-known connections between pathways and cancers.

Conclusions

Our results indicate that integrating differential gene expression with the interaction structure in a pathway is a powerful approach for detecting links between a cancer and the pathways perturbed in it. Our results also suggest that even well-studied pathways may be perturbed only partially in any given cancer. Further analysis of cancer-specific sub-pathways may shed new light on the similarities and differences between cancers.

Transcriptomic coordination in the human metabolic network reveals links between n-3 fat intake, adipose tissue gene expression and metabolic health

Understanding the molecular link between diet and health is a key goal in nutritional systems biology. As an alternative to pathway analysis, we have developed a joint multivariate and network-based approach to analysis of a dataset of habitual dietary records, adipose tissue transcriptomics and comprehensive plasma marker profiles from human volunteers with the Metabolic Syndrome. With this approach we identified prominent co-expressed sub-networks in the global metabolic network, which showed correlated expression with habitual n-3 PUFA intake and urinary levels of the oxidative stress marker 8-iso-PGF(2α). These sub-networks illustrated inherent cross-talk between distinct metabolic pathways, such as between triglyceride metabolism and production of lipid signalling molecules. In a parallel promoter analysis, we identified several adipogenic transcription factors as potential transcriptional regulators associated with habitual n-3 PUFA intake. Our results illustrate advantages of network-based analysis, and generate novel hypotheses on the transcriptomic link between habitual n-3 PUFA intake, adipose tissue function and oxidative stress.

COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method

MOTIVATION

The identification of condition specific sub-networks from gene expression profiles has important biological applications, ranging from the selection of disease-related biomarkers to the discovery of pathway alterations across different phenotypes. Although many methods exist for extracting these sub-networks, very few existing approaches simultaneously consider both the differential expression of individual genes and the differential correlation of gene pairs, losing potentially valuable information in the data.

RESULTS

In this article, we propose a new method, COSINE (COndition SpecIfic sub-NEtwork), which employs a scoring function that jointly measures the condition-specific changes of both 'nodes' (individual genes) and 'edges' (gene-gene co-expression). It uses the genetic algorithm to search for the single optimal sub-network which maximizes the scoring function. We applied COSINE to both simulated datasets with various differential expression patterns, and three real datasets, one prostate cancer dataset, a second one from the across-tissue comparison of morbidly obese patients and the other from the across-population comparison of the HapMap samples. Compared with previous methods, COSINE is more powerful in identifying truly significant sub-networks of appropriate size and meaningful biological relevance.

AVAILABILITY

The R code is available as the COSINE package on CRAN: http://cran.r-project.org/web/packages/COSINE/index.html.

ExprEssence--revealing the essence of differential experimental data in the context of an interaction/regulation net-work

Background

Experimentalists are overwhelmed by high-throughput data and there is an urgent need to condense information into simple hypotheses. For example, large amounts of microarray and deep sequencing data are becoming available, describing a variety of experimental conditions such as gene knockout and knockdown, the effect of interventions, and the differences between tissues and cell lines.

Results

To address this challenge, we developed a method, implemented as a Cytoscape plugin called ExprEssence. As input we take a network of interaction, stimulation and/or inhibition links between genes/proteins, and differential data, such as gene expression data, tracking an intervention or development in time. We condense the network, highlighting those links across which the largest changes can be observed. Highlighting is based on a simple formula inspired by the law of mass action. We can interactively modify the threshold for highlighting and instantaneously visualize results. We applied ExprEssence to three scenarios describing kidney podocyte biology, pluripotency and ageing: 1) We identify putative processes involved in podocyte (de-)differentiation and validate one prediction experimentally. 2) We predict and validate the expression level of a transcription factor involved in pluripotency. 3) Finally, we generate plausible hypotheses on the role of apoptosis, cell cycle deregulation and DNA repair in ageing data obtained from the hippocampus.

Conclusion

Reducing the size of gene/protein networks to the few links affected by large changes allows to screen for putative mechanistic relationships among the genes/proteins that are involved in adaptation to different experimental conditions, yielding important hypotheses, insights and suggestions for new experiments. We note that we do not focus on the identification of 'active subnetworks'. Instead we focus on the identification of single links (which may or may not form subnetworks), and these single links are much easier to validate experimentally than submodules. ExprEssence is available at http://sourceforge.net/projects/expressence/.

A systems biology approach to identify effective cocktail drugs

BACKGROUND

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

RESULTS

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

CONCLUSIONS

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

ERK regulates strain-induced migration and proliferation from different subcellular locations

Repetitive deformation like that engendered by peristalsis or villous motility stimulates intestinal epithelial proliferation on collagenous substrates and motility across fibronectin, each requiring ERK. We hypothesized that ERK acts differently at different intracellular sites. We stably transfected Caco-2 cells with ERK decoy expression vectors that permit ERK activation but interfere with its downstream signaling. Targeting sequences constrained the decoy inside or outside the nucleus. We assayed proliferation by cell counting and migration by circular wound closure with or without 10% repetitive deformation at 10 cycles/min. Confocal microscopy confirmed localization of the fusion proteins. Inhibition of phosphorylation of cytoplasmic RSK or nuclear Elk confirmed functionality. Both the nuclear-localized and cytosolic-localized ERK decoys prevented deformation-induced proliferation on collagen. Deformation-induced migration on fibronectin was prevented by constraining the decoy in the nucleus but not in the cytosol. Like the nuclear-localized ERK decoy, a Sef-overexpressing adenovirus that sequesters ERK in the cytoplasm also blocked the motogenic and mitogenic effects of strain. Inhibiting RSK or reducing Elk ablated both the mitogenic and motogenic effects of strain. RSK isoform reduction revealed isoform specificity. These results suggest that ERK must translocate to the nucleus to stimulate cell motility while ERK must act in both the cytosol and the nucleus to stimulate proliferation in response to strain. Selectively targeting ERK within different subcellular compartments may modulate or replace physical force effects on the intestinal mucosa to maintain the intestinal mucosal barrier in settings when peristalsis or villous motility are altered and fibronectin is deposited into injured tissue.

Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis

BACKGROUND

Cell responses to environmental stimuli are usually organized as relatively separate responsive gene modules at the molecular level. Identification of responsive gene modules rather than individual differentially expressed (DE) genes will provide important information about the underlying molecular mechanisms. Most of current methods formulate module identification as an optimization problem: find the active sub-networks in the genome-wide gene network by maximizing the objective function considering the gene differential expression and/or the gene-gene co-expression information. Here we presented a new formulation of this task: a group of closely-connected and co-expressed DE genes in the gene network are regarded as the signatures of the underlying responsive gene modules; the modules can be identified by finding the signatures and then recovering the "missing parts" by adding the intermediate genes that connect the DE genes in the gene network.

RESULTS

ClustEx, a two-step method based on the new formulation, was developed and applied to identify the responsive gene modules of human umbilical vein endothelial cells (HUVECs) in inflammation and angiogenesis models by integrating the time-course microarray data and genome-wide PPI data. It shows better performance than several available module identification tools by testing on the reference responsive gene sets. Gene set analysis of KEGG pathways, GO terms and microRNAs (miRNAs) target gene sets further supports the ClustEx predictions.

CONCLUSION

Taking the closely-connected and co-expressed DE genes in the condition-specific gene network as the signatures of the underlying responsive gene modules provides a new strategy to solve the module identification problem. The identified responsive gene modules of HUVECs and the corresponding enriched pathways/miRNAs provide useful resources for understanding the inflammatory and angiogenic responses of vascular systems.

APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

BACKGROUND

It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.

RESULTS

In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods.

CONCLUSION

We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.