Uncovering signal transduction networks from high-throughput data by integer linear programming

Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet

Understanding the biological functions and processes of genes, particularly those not yet characterized, is crucial for advancing molecular biology and identifying therapeutic targets. The hypothesis guiding this study is that the 3D proximity of genes correlates with their functional interactions and relevance in prokaryotes. We introduced 3D-GeneNet, an innovative software tool that utilizes high-throughput sequencing data from chromosome conformation capture techniques and integrates topological metrics to construct gene association networks. Through a series of comparative analyses focused on spatial versus linear distances, we explored various dimensions such as topological structure, functional enrichment levels, distribution patterns of linear distances among gene pairs, and the area under the receiver operating characteristic curve by utilizing model organism Escherichia coli K-12. Furthermore, 3D-GeneNet was shown to maintain good accuracy compared to multiple algorithms (neighbourhood, co-occurrence, coexpression, and fusion) across multiple bacteria, including E. coli, Brucella abortus, and Vibrio cholerae. In addition, the accuracy of 3D-GeneNet's prediction of long-distance gene interactions was identified by bacterial two-hybrid assays on E. coli K-12 MG1655, where 3D-GeneNet not only increased the accuracy of linear genomic distance tripled but also achieved 60% accuracy by running alone. Finally, it can be concluded that the applicability of 3D-GeneNet will extend to various bacterial forms, including Gram-negative, Gram-positive, single-, and multi-chromosomal bacteria through Hi-C sequencing and analysis. Such findings highlight the broad applicability and significant promise of this method in the realm of gene association network. 3D-GeneNet is freely accessible at https://github.com/gaoyuanccc/3D-GeneNet.

A multi-source molecular network representation model for protein-protein interactions prediction

The prediction of potential protein-protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein-protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein-protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein-protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs .

PSF toolkit: an R package for pathway curation and topology-aware analysis

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

Sub-zero microRNA expression in the liver of the frozen hatchling painted turtle, Chrysemys picta marginata

The Midland painted turtle (Chrysemys picta marginata) are the highest known vertebrate species to experience and survive freezing and sub-zero temperatures. Painted turtles typically hatch from their eggs in the fall and remain underground in their nests until the following spring. While in these nests over the winter, hatchling turtles withstand over 50 % of their total extracellular body water freezing. Herein, the expression of microRNAs (miRNAs) was investigated in response to freezing stress in the hatchling painted turtle liver. A total of 204 known miRNAs were identified to be expressed in turtles, with 17 being upregulated and 13 being downregulated during freezing. KEGG and GO analyses suggested that upregulated miRNAs inhibit genes of cell cycle and Focal adhesion and Adherens junction, suggesting their role in downregulation of central metabolic processes necessary for metabolic rate depression (MRD) and maintaining the tissue homeostasis. Only 9 of the 36 enriched KEGG pathways were less targeted by miRNAs during freezing, including linoleic acid metabolism and multiple signaling pathways. These predicted upregulated pathways likely promote homeoviscous adaptation and expression of pro-survival/protective proteins for metabolic adaptations necessary for defence of liver during MRD. Overall, miRNA-seq analysis of liver revealed a strong role of miRNA in the adaptive strategy that not only enables hatchlings to substantially suppress their nonessential energy needs but also makes them flexible enough to restore and protect their basal organ functions by activating pro-survival processes.

Phosprof: pathway analysis database of drug response based on phosphorylation activity measurements

Protein phosphorylation plays a fundamental role in many cellular processes. Proteins are phosphorylated by kinases, which have been studied as drug targets for the treatment of various diseases, particularly cancer. Because kinases have multiple roles in interconnected molecular pathways, their specific regulation is required to enhance beneficial and reduce adversarial effects of drugs. Using our previously developed platform, we measured phosphorylation profiles of MCF7 and K562 cells treated with 94 clinical drugs. These phosphorylation profiles can provide insights into pathway activities and biological functions. Here, we introduce Phosprof, a novel database of drug response based on phosphorylation activity. Phosprof is able to present up- or downregulated phosphorylated signature proteins on pathway maps, significant pathways on the hierarchal tree in signal transduction and commonly perturbed pathways affected by the selected drugs. It also serves as a useful web interface for new or known drug profile search based on their molecular similarity with the 94 drugs. Phosprof can be helpful for further investigation of drug responses in terms of phosphorylation by utilizing the various approved drugs whose target phenotypes are known.

DATABASE URL

https://phosprof.medals.jp/.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.

Secondary structure prediction of protein based on multi scale convolutional attention neural networks

To fully extract the local and long-range information of amino acid sequences and enhance the effective information, this research proposes a secondary structure prediction model of protein based on a multi-scale convolutional attentional neural network. The model uses a multi-channel multi-scale parallel architecture to extract amino acid structure features of different granularity according to the window size. The reconstructed feature maps are obtained via multiple convolutional attention blocks. Then, the reconstructed feature map is fused with the input feature map to obtain the enhanced feature map. Finally, the enhanced feature map is fed to the Softmax classifier for prediction. While the traditional cross-entropy loss cannot effectively solve the problem of non-equilibrium training samples, a modified correlated cross-entropy loss function may alleviate this problem. After numerous comparison and ablation experiments, it is verified that the improved model can indeed effectively extract amino acid sequence feature information, alleviate overfitting, and thus improve the overall prediction accuracy.

Identifying intracellular signaling modules and exploring pathways associated with breast cancer recurrence

Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .

Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole-cell model WM_S288C

Biological network construction for Saccharomyces cerevisiae is a widely used approach for simulating phenotypes and designing cell factories. However, due to a complicated regulatory mechanism governing the translation of genotype to phenotype, precise prediction of phenotypes remains challenging. Here, we present WM_S288C, a computational whole-cell model that includes 15 cellular states and 26 cellular processes and which enables integrated analyses of physiological functions of Saccharomyces cerevisiae. Using WM_S288C to predict phenotypes of S. cerevisiae, the functions of 1140 essential genes were characterized and linked to phenotypes at five levels. During the cell cycle, the dynamic allocation of intracellular molecules could be tracked in real-time to simulate cell activities. Additionally, one-third of non-essential genes were identified to affect cell growth via regulating nucleotide concentrations. These results demonstrated the value of WM_S288C as a tool for understanding and investigating the phenotypes of S. cerevisiae.

pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

Conceptual Evolution of Cell Signaling

During the last 100 years, cell signaling has evolved into a common mechanism for most physiological processes across systems. Although the majority of cell signaling principles were initially derived from hormonal studies, its exponential growth has been supported by interdisciplinary inputs, e.g., from physics, chemistry, mathematics, statistics, and computational fields. As a result, cell signaling has grown out of scope for any general review. Here, we review how the messages are transferred from the first messenger (the ligand) to the receptor, and then decoded with the help of cascades of second messengers (kinases, phosphatases, GTPases, ions, and small molecules such as cAMP, cGMP, diacylglycerol, etc.). The message is thus relayed from the membrane to the nucleus where gene expression ns, subsequent translations, and protein targeting to the cell membrane and other organelles are triggered. Although there are limited numbers of intracellular messengers, the specificity of the response profiles to the ligands is generated by the involvement of a combination of selected intracellular signaling intermediates. Other crucial parameters in cell signaling are its directionality and distribution of signaling strengths in different pathways that may crosstalk to adjust the amplitude and quality of the final effector output. Finally, we have reflected upon its possible developments during the coming years.

Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks

Identification of new drug and cell therapy targets for disease treatment will be facilitated by a detailed molecular understanding of normal and disease development. Human pluripotent stem cells can provide a large in vitro source of human cell types and, in a growing number of instances, also three-dimensional multicellular tissues called organoids. The application of stem cell technology to discovery and development of new therapies will be aided by detailed molecular characterisation of cell identity, cell signalling pathways and target gene networks. Big data or 'omics' techniques-particularly transcriptomics and proteomics-facilitate cell and tissue characterisation using thousands to tens-of-thousands of genes or proteins. These gene and protein profiles are analysed using existing and/or emergent bioinformatics methods, including a growing number of methods that compare sample profiles against compendia of reference samples. This review assesses how compendium-based analyses can aid the application of stem cell technology for new therapy development. This includes via robust definition of differentiated stem cell identity, as well as elucidation of complex signalling pathways and target gene networks involved in normal and diseased states.

Identification of active signaling pathways by integrating gene expression and protein interaction data

Background

Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors.

Results

Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development.

Conclusions

The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0655-x) contains supplementary material, which is available to authorized users.

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways

Background

Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer.

Results

We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences.

Conclusions

Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data

Background

Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data.

Results

We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high.

Conclusion

We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models.

PhosD: inferring kinase-substrate interactions based on protein domains

Motivation

Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved.

Results

In this paper, we propose a novel probabilistic model named as PhosD to predict kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. By further taking into account protein-protein interactions, our PhosD outperforms other popular approaches on several benchmark datasets with higher precision. In addition, some of our predicted kinase-substrate relationships are validated by signaling pathways, indicating the predictive power of our approach. Furthermore, we notice that given a kinase, the more substrates are known for the kinase the more accurate its predicted substrates will be, and the domains involved in kinase-substrate interactions are found to be more conserved across proteins phosphorylated by multiple kinases. These findings can help develop more efficient computational approaches in the future.

Availability and Implementation

The data and results are available at http://comp-sysbio.org/phosd.

Contact

xm_zhao@tongji.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Signalling pathway impact analysis based on the strength of interaction between genes

Signalling pathway analysis is a popular approach that is used to identify significant cancer‐related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (−1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer‐related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.

Mutated genes and driver pathways involved in myelodysplastic syndromes—a transcriptome sequencing based approach

Myelodysplastic syndromes are a heterogeneous group of clonal disorders of hematopoietic progenitors and have potentiality to progress into acute myelogenous leukemia. Development of effective treatments has been impeded by limited insight into pathogenic pathways. In this study, we applied RNA-seq technology to study the transcriptome on 20 MDS patients and 5 age-matched controls, and developed a pipeline for analyzing this data. After analysis, we identified 38 mutated genes contributing to MDS pathogenesis. 37 out of 38 genes have not been reported previously, suggesting our pipeline is critical for identifying novel mutated genes in MDS. The most recurrent mutation happened in gene IFRD1, which involved 30% of patient samples. Biological relationships among these mutated genes were mined using Ingenuity Pathway Analysis, and the results demonstrated that top two networks with highest scores were highly associated with cancer and hematological diseases, indicating that the mutated genes identified by our method were highly relevant to MDS. We then integrated the pathways in KEGG database and the identified mutated genes using our novel rule-based mutated driver pathway scoring approach for detecting mutated driver pathways. The results indicated two mutated driver pathways are important for the pathogenesis of MDS: pathway in cancer and in regulation of actin cytoskeleton. The latter, which likely contributes to the hallmark morphologic dysplasia observed in MDS, has not been reported, to the best of our knowledge. These results provide us new insights into the pathogenesis of MDS, which, in turn, may lead to novel therapeutics for this disease.

Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

Identify asthma genes across three phases based on protein-protein interaction network

Asthma is a common inflammatory disease that is generally caused by genetic mutations or environmental factors. Recently, the emerging of omics data provides an alternative way to understand asthma. In this study, the authors present a new framework to detect asthma disease genes based on protein-protein interaction network (PPIN) and gene expression. Specifically, they construct PPINs for different stages of asthma, and detect those interactions occurred in the specific stages. By investigating the proteins in these stage-specific interactions, they find they are more likely related to asthma, and the functional enrichment analysis indicate that the pathways enriched in the differential interactions are related to the progress of asthma. Moreover, some proteins in the differential interactions have been previously reported to be related to asthma in the literature, implying the usefulness of the proposed approach.

A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks

The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback-Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.

P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations

Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

Pathway crosstalk analysis of microarray gene expression profile in human hepatocellular carcinoma

Liver cancer is the third most common cause of cancer death in the world. Hepatocellular carcinoma (HCC) is the main pathological types in liver cancer, which amounts to 70-85 % of primary liver cancer in the world and 90 % in China. The aim of this study was to establish a PPI network and a pathway crosstalk network to isolate important dysfunctional pathways which play an important role in the pathogenesis of HCC. System biology approach was used in this research. A PPI network was firstly built and then a dysfunctional crosstalk network of HCC related pathways was constructed. Several important significant dysfunctional crosstalk pathways were identified. Basal transcription factors (hsa03022), Glycerophospholipid metabolism (hsa00564) and Metabolism of xenobiotics by cytochrome P450 (hsa00980) were significantly interact with Pathway in cancer (hsa05200). Besides, pathway Axon guidance (hsa04360) was also dysfunctional crosstalk with Pathway in cancer (hsa05200). The crosstalks among these pathways reveal some evidence that the pathways closely cooperated and play important tasks in HCC progression. Besides, the pathway hsa04360 dysfunctional crosstalk with the hsa05200 indicates there would be a same mechanism for HCC invasion and migration.

Functional module search in protein networks based on semantic similarity improves the analysis of proteomics data

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system.

Inferring host gene subnetworks involved in viral replication

Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at http://www.biostat.wisc.edu/~craven/chasman_host_virus/.

Improved multi-level protein-protein interaction prediction with semantic-based regularization

Background

Protein–protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels.

Results

Inspired by earlier ideas of Yip et al. (BMC Bioinformatics 10:241, 2009), in the present paper we view the problem as a multi-level learning task, with one task per level (proteins, domains and residues), and propose a machine learning method that collectively infers the binding state of all object pairs. Our method is based on Semantic Based Regularization (SBR), a flexible and theoretically sound machine learning framework that uses First Order Logic constraints to tie the learning tasks together. We introduce a set of biologically motivated rules that enforce consistent predictions between the hierarchy levels.

Conclusions

We study the empirical performance of our method using a standard validation procedure, and compare its performance against the only other existing multi-level prediction technique. We present results showing that our method substantially outperforms the competitor in several experimental settings, indicating that exploiting the hierarchical nature of the problem can lead to better predictions. In addition, our method is also guaranteed to produce interactions that are consistent with respect to the protein–domain–residue hierarchy.

Integrating gene expression and protein interaction data for signaling pathway prediction of Alzheimer's disease

Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer's disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF-κB (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-κB and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge-based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.

Identification of human disease genes from interactome network using graphlet interaction

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

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.

Transcriptional regulatory network and protein-protein interaction to reveal the mechanism of pancreatic cancer

The development of pancreatic cancer (PC) may involve the over-expression of oncogenes, inactivation tumor suppressor genes or the deregulation of various signaling proteins. Thus identification and analysis of transcriptional regulatory relationship as well as protein-protein interaction (PPI) in PC to provide deep insights into the pathogenetic mechanism of pancreatic cancer. In this study, we downloaded the gene expression profile of PC from Gene Expression Omnibus and identified differentially expressed genes (DEGs) in PC patients compared with controls. To further understand how these DEGs act together to account for the initiation of pancreatic cancer, a transcriptional regulatory network was constructed to find the notes for GO function and KEGG pathways annotation, aiming to explore the clusters and pathways in PC. A total of 1,821 transcriptional regulatory relationships were identified. Then, a PPI network was constructed and noted by GO and KEGG, and some special modules, clusters and pathways were identified to involved in PC. Finally, we constructed the transcriptional regulatory network and PPI network of pancreatic cancer. Comparing the pathways involved in Transcriptional regulatory network and PPI network, pathway in cancer, PC, p53 signaling pathway, Hematopoietic cell lineage and graft-versus-host disease co-existed in these two network, so we predict these pathways may play key factors in development of cancer.

Detection of deregulated modules using deregulatory linked path

The identification of deregulated modules (such as induced by oncogenes) is a crucial step for exploring the pathogenic process of complex diseases. Most of the existing methods focus on deregulation of genes rather than the links of the path among them. In this study, we emphasize on the detection of deregulated links, and develop a novel and effective regulatory path-based approach in finding deregulated modules. Observing that a regulatory pathway between two genes might involve in multiple rather than a single path, we identify condition-specific core regulatory path (CCRP) to detect the significant deregulation of regulatory links. Using time-series gene expression, we define the regulatory strength within each gene pair based on statistical dependence analysis. The CCRPs in regulatory networks can then be identified using the shortest path algorithm. Finally, we derive the deregulated modules by integrating the differential edges (as deregulated links) of the CCRPs between the case and the control group. To demonstrate the effectiveness of our approach, we apply the method to expression data associated with different states of Human Epidermal Growth Factor Receptor 2 (HER2). The experimental results show that the genes as well as the links in the deregulated modules are significantly enriched in multiple KEGG pathways and GO biological processes, most of which can be validated to suffer from impact of this oncogene based on previous studies. Additionally, we find the regulatory mechanism associated with the crucial gene SNAI1 significantly deregulated resulting from the activation of HER2. Hence, our method provides not only a strategy for detecting the deregulated links in regulatory networks, but also a way to identify concerning deregulated modules, thus contributing to the target selection of edgetic drugs.

eFG: an electronic resource for Fusarium graminearum

Fusarium graminearum is a plant pathogen, which causes crop diseases and further leads to huge economic damage worldwide in past decades. Recently, the accumulation of different types of molecular data provides insights into the pathogenic mechanism of F. graminearum, and might help develop efficient strategies to combat this destructive fungus. Unfortunately, most available molecular data related to F. graminearum are distributed in various media, where each single source only provides limited information on the complex biological systems of the fungus. In this work, we present a comprehensive database, namely eFG (Electronic resource for Fusarium graminearum), to the community for further understanding this destructive pathogen. In particular, a large amount of functional genomics data generated by our group is deposited in eFG, including protein subcellular localizations, protein-protein interactions and orthologous genes in other model organisms. This valuable knowledge can not only help to disclose the molecular underpinnings of pathogenesis of the destructive fungus F. graminearum but also help the community to develop efficient strategies to combat this pathogen. To our best knowledge, eFG is the most comprehensive functional genomics database for F. graminearum until now. The eFG database is freely accessible at http://csb.shu.edu.cn/efg/ with a user-friendly and interactive interface, and all data can be downloaded freely. DATABASE URL: http://csb.shu.edu.cn/efg/

Pathway crosstalk analysis based on protein-protein network analysis in ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death in women aged 35 to 74 years. Although there are several popular hypothesis of ovarian cancer pathogenesis, the genetic mechanisms are far from being clear. Recently, systems biology approaches such as network-based methods have been successfully applied to elucidate the mechanisms of diseases. In this study, we constructed a crosstalk network among ovarian cancer related pathways by integrating protein-protein interactions and KEGG pathway information. Several significant pathways were identified to crosstalk with each other in ovarian cancer, such as the chemokine, Notch, Wnt and NOD-like receptor signaling pathways. Results from these studies will provide the groundwork for a combination therapy approach targeting multiple pathways which will likely be more effective than targeting one pathway alone.

A systems biology approach to identify the signalling network regulated by Rho-GDI-γ during neural stem cell differentiation

Understanding the molecular mechanism that underlies the differentiation of neural stem cells (NSCs) is vital to develop regenerative medicines for neurological disorders. In our previous work, Rho-GDI-γ was found to be able to prompt neuronal differentiation when it was down regulated. However, it is unclear how Rho-GDI-γ regulates this differentiation process. Therefore, a novel systems biology approach is presented here to identify putative signalling pathways regulated by Rho-GDI-γ during NSC differentiation, and these pathways can provide insights into the NSC differentiation mechanisms. In particular, our proposed approach combines the predictive power of computational biology and molecular experiments. With different biological experiments, the genes in the computationally identified signalling network were validated to be indeed regulated by Rho-GDI-γ during the differentiation of NSCs. In particular, one randomly selected pathway involving Vcp, Mapk8, Ywhae and Ywhah was experimentally verified to be regulated by Rho-GDI-γ. These promising results demonstrate the effectiveness of our proposed systems biology approach, indicating the potential predictive power of integrating computational and experimental approaches.