The Biomolecular Interaction Network Database and related tools 2005 update

Genes dysregulated to different extent or oppositely in estrogen receptor-positive and estrogen receptor-negative breast cancers

Background

Directly comparing gene expression profiles of estrogen receptor-positive (ER+) and estrogen receptor-negative (ER−) breast cancers cannot determine whether differentially expressed genes between these two subtypes result from dysregulated expression in ER+ cancer or ER− cancer versus normal controls, and thus would miss critical information for elucidating the transcriptomic difference between the two subtypes.

Principal Findings

Using microarray datasets from TCGA, we classified the genes dysregulated in both ER+ and ER− cancers versus normal controls into two classes: (i) genes dysregulated in the same direction but to a different extent, and (ii) genes dysregulated to opposite directions, and then validated the two classes in RNA-sequencing datasets of independent cohorts. We showed that the genes dysregulated to a larger extent in ER+ cancers than in ER− cancers enriched in glycerophospholipid and polysaccharide metabolic processes, while the genes dysregulated to a larger extent in ER− cancers than in ER+ cancers enriched in cell proliferation. Phosphorylase kinase and enzymes of glycosylphosphatidylinositol (GPI) anchor biosynthesis were upregulated to a larger extent in ER+ cancers than in ER− cancers, whereas glycogen synthase and phospholipase A2 were downregulated to a larger extent in ER+ cancers than in ER− cancers. We also found that the genes oppositely dysregulated in the two subtypes significantly enriched with known cancer genes and tended to closely collaborate with the cancer genes. Furthermore, we showed the possibility that these oppositely dysregulated genes could contribute to carcinogenesis of ER+ and ER− cancers through rewiring different subpathways.

Conclusions

GPI-anchor biosynthesis and glycogenolysis were elevated and hydrolysis of phospholipids was depleted to a larger extent in ER+ cancers than in ER− cancers. Our findings indicate that the genes oppositely dysregulated in the two subtypes are potential cancer genes which could contribute to carcinogenesis of both ER+ and ER− cancers through rewiring different subpathways.

Predicting potential cancer genes by integrating network properties, sequence features and functional annotations

The discovery of novel cancer genes is one of the main goals in cancer research. Bioinformatics methods can be used to accelerate cancer gene discovery, which may help in the understanding of cancer and the development of drug targets. In this paper, we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence, including protein-protein interaction network properties, and sequence and functional features. We detected 55 features that were significantly different between cancer genes and non-cancer genes. Fourteen cancer-associated features were chosen to train the classifier. Four machine learning methods, logistic regression, support vector machines (SVMs), BayesNet and decision tree, were explored in the classifier models to distinguish cancer genes from non-cancer genes. The prediction power of the different models was evaluated by 5-fold cross-validation. The area under the receiver operating characteristic curve for logistic regression, SVM, Baysnet and J48 tree models was 0.834, 0.740, 0.800 and 0.782, respectively. Finally, the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database, and 1976 cancer gene candidates were identified. We found that the integrated prediction model performed much better than the models based on the individual biological evidence, and the network and functional features had stronger powers than the sequence features in predicting cancer genes.

Finding the targets of a drug by integration of gene expression data with a protein interaction network

Polypharmacology, which focuses on designing drugs that bind efficiently to multiple targets, has emerged as a new strategic trend in today's drug discovery research. Many successful drugs achieve their effects via multi-target interactions. However, these targets are largely unknown for both marketed drugs and drugs in development. A better knowledge of a drug's mode of action could be of substantial value to future drug development, in particular for side effect prediction and drug repositioning. We propose a network-based computational method for drug target prediction, applicable on a genome-wide scale. Our approach relies on the analysis of gene expression following drug treatment in the context of a functional protein association network. By diffusing differential expression signals to neighboring or correlated nodes in the network, genes are prioritized as potential targets based on the transcriptional response of functionally related genes. Different diffusion strategies were evaluated on 235 publicly available gene expression datasets for treatment with bioactive molecules having a known target. AUC values of up to more than 90% demonstrate the effectiveness of our approach and indicate the predictive power of integrating experimental gene expression data with prior knowledge from protein association networks.

Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

Improving protein complex classification accuracy using amino acid composition profile

Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain.

Arabidopsis scaffold protein RACK1A interacts with diverse environmental stress and photosynthesis related proteins

Scaffold proteins are known to regulate important cellular processes by interacting with multiple proteins to modulate molecular responses. RACK1 (Receptor for Activated C Kinase 1) is a WD-40 type scaffold protein, conserved in eukaryotes, from Chlamydymonas to plants and humans, expresses ubiquitously and plays regulatory roles in diverse signal transduction and stress response pathways. Here we present the use of Arabidopsis RACK1A, the predominant isoform of a 3-member family, as a bait to screen a split-ubiquitin based cDNA library. In total 97 proteins from dehydration, salt stress, ribosomal and photosynthesis pathways are found to potentially interact with RACK1A. False positive interactions were eliminated following extensive selection based growth potentials. Confirmation of a sub-set of selected interactions is demonstrated through the co-transformation with individual plasmid containing cDNA and the respective bait. Interaction of diverse proteins points to a regulatory role of RACK1A in the cross-talk between signaling pathways. Promoter analysis of the stress and photosynthetic pathway genes revealed conserved transcription factor binding sites. RACK1A is known to be a multifunctional protein and the current identification of potential interacting proteins and future in vivo elucidations of the physiological basis of such interactions will shed light on the possible molecular mechanisms that RACK1A uses to regulate diverse signaling pathways.

An integrated proteomics and bioinformatics analyses of hepatitis B virus X interacting proteins and identification of a novel interactor apoA-I

HBx is well-known to be a multifunctional protein encoded by HBV and its biological functions are mainly dependent on pleiotropic protein-protein interactions (PPIs); however, the global mapping of HBx-interactome has not been established so far. Thus, in this study, we have identified 127 HBx-interacting proteins by a profound GST pull-down assay coupled with mass spectrometry, and constructed an HBx-interactome network and core apoA-I pathways with a series of bioinformatics approaches. One of the identified HBx-binding partners is apolipoprotein A-I (apoA-I), which has a specific role in lipid and cholesterol metabolism. The HBx-apoA-I protein interaction was confirmed by both GST pull-down and co-immunoprecipitation. The ectopic overexpression of apoA-I can lead to a significant inhibition on HBV secretion concomitant with the reduction of cellular cholesterol level. In addition, HBV can modulate the function of apoA-I through HBx which might interact with the 44-189 residues of apoA-I and result in dysfunction of apoA-I such as decreased self-association ability, increased carbonyl level and impaired lipid-binding ability. Our results demonstrate an integrated physical association of HBx and host proteins, especially a novel interactor apoA-I that may influence the HBV secretion, which would shed new light on exploring the complicated mechanisms of HBV manipulation on host cellular functions.

BIOLOGICAL SIGNIFICANCE

HBx is well-known to be a multifunctional protein encoded by HBV and its biological functions are mainly dependent on pleiotropic protein-protein interactions. Although a series of HBx-interacting proteins have been identified, a global characterization of HBx interactome has not been reported. In this study, we have identified a total of 127 HBx-interacting proteins by a profound GST pull-down assay coupled with mass spectrometry, and constructed an HBx-interactome network with a series of bioinformatics approaches. Our results demonstrate an integrated physical association of HBx and host proteins which may help us explore the complicated mechanisms of HBV manipulation on host cellular functions. In addition, we validated one of the identified HBx-binding partners, apolipoprotein A-I (apoA-I), which played a significant inhibitory effect on HBV secretion, indicating a crucial role of the HBx-apoA-I axis in HBV life cycle.

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Efficient Computational Construction of Weighted Protein-Protein Interaction Networks Using Adaptive Filtering Techniques Combined with Natural Selection-Based Heuristic Algorithms

The analysis of protein-protein interactions (PPIs) is crucial to the understanding of cellular processes. In recent years, a variety of computational methods have been developed to supplement the interactions that have been detected experimentally. The article’s main objective is to present a novel classification framework for predicting PPIs combining the advantages of two algorithmic methods’ categories. State-of-the-art adaptive filtering techniques were combined with the most contemporary heuristic methods which are based in the natural selection process. The authors’ goal is to find a simple mathematical equation that governs the best classifier enabling the extraction of biological knowledge. The proposed methodology assigns a confidence score to each protein pair and as a result a weighted PPI network is constructed. All possible combinations of the selected adaptive filtering and heuristic techniques were used and comparisons were made to explore the classifiers with the highest performance and interpretability.

Effects of early life exposure to ultraviolet C radiation on mitochondrial DNA content, transcription, ATP production, and oxygen consumption in developing Caenorhabditis elegans

Background

Mitochondrial DNA (mtDNA) is present in multiple copies per cell and undergoes dramatic amplification during development. The impacts of mtDNA damage incurred early in development are not well understood, especially in the case of types of mtDNA damage that are irreparable, such as ultraviolet C radiation (UVC)-induced photodimers.

Methods

We exposed first larval stage nematodes to UVC using a protocol that results in accumulated mtDNA damage but permits nuclear DNA (nDNA) repair. We then measured the transcriptional response, as well as oxygen consumption, ATP levels, and mtDNA copy number through adulthood.

Results

Although the mtDNA damage persisted to the fourth larval stage, we observed only a relatively minor ~40% decrease in mtDNA copy number. Transcriptomic analysis suggested an inhibition of aerobic metabolism and developmental processes; mRNA levels for mtDNA-encoded genes were reduced ~50% at 3 hours post-treatment, but recovered and, in some cases, were upregulated at 24 and 48 hours post-exposure. The mtDNA polymerase γ was also induced ~8-fold at 48 hours post-exposure. Moreover, ATP levels and oxygen consumption were reduced in response to UVC exposure, with marked reductions of ~50% at the later larval stages.

Conclusions

These results support the hypothesis that early life exposure to mitochondrial genotoxicants could result in mitochondrial dysfunction at later stages of life, thereby highlighting the potential health hazards of time-delayed effects of these genotoxicants in the environment.

Identification of novel caspase/autophagy-related gene switch to cell fate decisions in breast cancers

OBJECTIVES

Caspases, a family of cysteine proteases with unique substrate specificities, contribute to apoptosis, whereas autophagy-related genes (ATGs) regulate cytoprotective autophagy or autophagic cell death in cancer. Accumulating evidence has recently revealed underlying mechanisms of apoptosis and autophagy; however, their intricate relationships still remain to be clarified. Identification of caspase/ATG switches between apoptosis and autophagy may address this problem.

MATERIALS AND METHODS

Identification of caspase/ATG switches was carried out using a series of elegant systems biology & bioinformatics approaches, such as network construction, hub protein identification, microarray analyses, targeted microRNA prediction and molecular docking.

RESULTS

We computationally constructed the global human network from several online databases and further modified it into the basic caspase/ATG network. On the basis of apoptotic or autophagic gene differential expressions, we identified three molecular switches [including androgen receptor, serine/threonine-protein kinase PAK-1 (PAK-1) and mitogen-activated protein kinase-3 (MAPK-3)] between certain caspases and ATGs in human breast carcinoma MCF-7 cells. Subsequently, we identified microRNAs (miRNAs) able to target androgen receptor, PAK-1 and MAPK-3, respectively. Ultimately, we screened a range of small molecule compounds from DrugBank, able to target the three above-mentioned molecular switches in breast cancer cells.

CONCLUSIONS

We have systematically identified novel caspase/ATG switches involved in miRNA regulation, and predicted targeted anti-cancer drugs. These findings may uncover intricate relationships between apoptosis and autophagy and thus provide further new clues towards possible cancer drug discovery.

Quantitative analysis of murine terminal erythroid differentiation in vivo: novel method to study normal and disordered erythropoiesis

Terminal erythroid differentiation is the process during which proerythroblasts differentiate to produce enucleated reticulocytes. Although it is well established that during murine erythropoiesis in vivo, 1 proerythroblast undergoes 3 mitosis to generate sequentially 2 basophilic, 4 polychromatic, and 8 orthochromatic erythroblasts, currently there is no method to quantitatively monitor this highly regulated process. Here we outline a method that distinguishes each distinct stage of erythroid differentiation in cells from mouse bone marrow and spleen based on expression levels of TER119, CD44, and cell size. Quantitative analysis revealed that the ratio of proerythroblasts:basophilic:polychromatic:orthromatic erythroblasts follows the expected 1:2:4:8 ratio, reflecting the physiologic progression of terminal erythroid differentiation in normal mice. Moreover, in 2 stress erythropoiesis mouse models, phlebotomy-induced acute anemia and chronic hemolytic anemia because of 4.1R deficiency, the ratio of these erythroblast populations remains the same as that of wild-type bone marrow. In contrast, in anemic β-thalassemia intermedia mice, there is altered progression which is restored to normal by transferrin treatment which was previously shown to ameliorate the anemic phenotype. The means to quantitate in vivo murine erythropoiesis using our approach will probably have broad application in the study of altered erythropoiesis in various red cell disorders.

Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era

Infectious diseases comprise some of the leading causes of death and disability worldwide. Interactions between pathogen and host proteins underlie the process of infection. Improved understanding of pathogen-host molecular interactions will increase our knowledge of the mechanisms involved in infection, and allow novel therapeutic solutions to be devised. Complete genome sequences for a number of pathogenic microorganisms, as well as the human host, has led to the revelation of their protein-protein interaction (PPI) networks. In this post-genomic era, pathogen-host interactions (PHIs) operating during infection can also be mapped. Detailed systematic analyses of PPI and PHI data together are required for a complete understanding of pathogenesis of infections. Here we review the striking results recently obtained during the construction and investigation of these networks. Emphasis is placed on studies producing large-scale interaction data by high-throughput experimental techniques.

Network-based analysis of multivariate gene expression data

Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes, where the expression levels of a give gene are expected to be dependent. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time; these genes can also point to the pathways that are perturbed during a given biological process. Several empirical Bayes approaches have been developed for identifying the differentially expressed genes in order to account for the parallel structure of the data and to borrow information across all the genes. However, these methods assume that the genes are independent. In this paper, we introduce an alternative empirical Bayes approach for analysis of multivariate gene expression data by assuming a discrete Markov random field (MRF) prior, where the dependency of the differential expression patterns of genes on the networks are modeled by a Markov random field. Simulation studies indicated that the method is quite effective in identifying genes and the modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway information, with similar observed false discovery rates. We applied the proposed methods for analysis of a microarray time course gene expression study of TrkA- and TrkB-transfected neuroblastoma cell lines and identified genes and subnetworks on MAPK, focal adhesion, and prion disease pathways that may explain cell differentiation in TrkA-transfected cell lines.

Computational analysis of virus-host interactomes

High-throughput methods for screening of physical and functional interactions now provide the means to study virus-host interactions on a genome scale. The limited coverage of these methods and the large size and uncertain quality of the identified interaction sets, however, require sophisticated computational approaches to obtain novel insights and hypotheses on virus infection processes from these interactions. Here, we describe the central steps of bioinformatics methods applied most commonly for this task and highlight important aspects that need to be considered and potential pitfalls that should be avoided.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

A vertex similarity-based framework to discover and rank orphan disease-related genes

Background

A rare or orphan disease (OD) is any disease that affects a small percentage of the population. While opportunities now exist to accelerate progress toward understanding the basis for many more ODs, the prioritization of candidate genes is still a critical step for disease-gene identification. Several network-based frameworks have been developed to address this problem with varied results.

Result

We have developed a novel vertex similarity (VS) based parameter-free prioritizing framework to identify and rank orphan disease candidate genes. We validate our approach by using 1598 known orphan disease-causing genes (ODGs) representing 172 orphan diseases (ODs). We compare our approach with a state-of-art parameter-based approach (PageRank with Priors or PRP) and with another parameter-free method (Interconnectedness or ICN). Our results show that VS-based approach outperforms ICN and is comparable to PRP. We further apply VS-based ranking to identify and rank potential novel candidate genes for several ODs.

Conclusion

We demonstrate that VS-based parameter-free ranking approach can be successfully used for disease candidate gene prioritization and can complement other network-based methods for candidate disease gene ranking. Importantly, our VS-ranked top candidate genes for the ODs match the known literature, suggesting several novel causal relationships for further investigation.

Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes

Background

Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D). However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes. There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.

Methods

We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes. Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. We then studied the relationship between these T2D genes and curated gene sets.

Results

We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20. Using the T2D genetic network, we identified highly-interconnected network "hub" genes. By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories. We found that highly-interconnected T2D disease network “hub” genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1. The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list. Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer. We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport. Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.

Conclusions

Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors. T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks. The framework developed for T2D studies may serve as a guide for studying other complex diseases.

Discovering pathway cross-talks based on functional relations between pathways

Background

In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations.

Results

This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis.

Conclusions

This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.

Arabidopsis scaffold protein RACK1A modulates rare sugar D-allose regulated gibberellin signaling

As energy sources and structural components, sugars are the central regulators of plant growth and development. In addition to the abundant natural sugars in plants, more than 50 different kinds of rare sugars exist in nature, several of which show distinct roles in plant growth and development. Recently, one of the rare sugars, D-allose, an epimer of D-glucose at C3, is found to suppress plant hormone gibberellin (GA) signaling in rice. Scaffold protein RACK1A in the model plant Arabidopsis is implicated in the GA pathway as rack1a knockout mutants show insensitivity to GA in GA-induced seed germination. Using genetic knockout lines and a reporter gene, the functional role of RACK1A in the D-allose pathway was investigated. It was found that the rack1a knockout seeds showed hypersensitivity to D-allose-induced inhibition of seed germination, implicating a role for RACK1A in the D-allose mediated suppression of seed germination. On the other hand, a functional RACK1A in the background of the double knockout mutations in the other two RACK1 isoforms, rack1b/rack1c, showed significant resistance to the D-allose induced inhibition of seed germination. The collective results implicate the RACK1A in the D-allose mediated seed germination inhibition pathway. Elucidation of the rare sugar signaling mechanism will help to advance understanding of this less studied but important cellular signaling pathway.

Molecular interaction databases

Molecular interaction databases are playing an ever more important role in our understanding of the biology of the cell. An increasing number of resources exist to provide these data and many of these have adopted the controlled vocabularies and agreed-upon standardised data formats produced by the Molecular Interaction workgroup of the Human Proteome Organization Proteomics Standards Initiative (HUPO PSI-MI). Use of these standards allows each resource to establish PSI Common QUery InterfaCe (PSICQUIC) service, making data from multiple resources available to the user in response to a single query. This cooperation between databases has been taken a stage further, with the establishment of the International Molecular Exchange (IMEx) consortium which aims to maximise the curation power of numerous data resources, and provide the user with a non-redundant, consistently annotated set of interaction data.

Identification of microRNA-regulated autophagic pathways in plant lectin-induced cancer cell death

OBJECTIVES

Plant lectins, carbohydrate-binding proteins of non-immune origin, have recently been reported to induce programmed cell death (including apoptosis and autophagy) in many types of cancer cells. MicroRNAs (miRNAs), small, non-coding endogenous RNAs, ~22 nucleotides (nt) in length, have been well characterized to play essential roles in regulation of the autophagy process in cancer; however, how these miRNAs regulate autophagic pathways in plant lectin-induced cancer cells, still remains an enigma.

MATERIALS AND METHODS

Identification of microRNA-regulated autophagic pathways was carried out using a series of elegant systems - biology and bioinformatics approaches, such as network construction, hub protein identification, targeted microRNA prediction, microarray analyses and molecular docking.

RESULTS

We computationally constructed the human autophagic protein-protein interaction (PPI) network, and further modified this network into a plant lectin-induced network. Subsequently, we identified 9 autophagic hub proteins and 13 relevant oncogenic and tumour suppressive miRNAs, that could regulate these aforementioned targeted autophagic hub proteins, in human breast carcinoma MCF-7 cells. In addition, we confirmed that plant lectins could block the sugar-containing receptor EGFR-mediated survival pathways, involved in autophagic hub proteins and relevant miRNAs, thereby ultimately culminating in autophagic cell death.

CONCLUSIONS

These results demonstrate that network-based identification of microRNAs modulate autophagic pathways in plant lectin-treated cancer cells, which may shed new light on the discovery of plant lectins as potent autophagic inducers, for cancer drug discovery.

Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks

Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.

Tissue-specific functional networks for prioritizing phenotype and disease genes

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

Tissue specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. We propose an effective strategy to model tissue-specific functional relationship networks in the laboratory mouse. We integrated large scale genomics datasets as well as low-throughput tissue-specific expression profiles to estimate the probability that two proteins are co-functioning in the tissue under study. These networks can accurately reflect the diversity of protein functions across different organs and tissue compartments. By computationally exploring the tissue-specific networks, we can accurately predict novel phenotype-related gene candidates. We experimentally confirmed a top candidate gene, Mybl1, to affect several male fertility phenotypes, predicted based on male-reproductive system-specific networks and we predicted candidates related to a rare genetic disease ataxia, which are supported by experimental and literature evidence. The above results demonstrate the power of modeling tissue-specific dynamics of co-functionality through computational approaches.

Multiple biomarker tissue arrays: A computational approach to identifying protein-protein interactions in the EGFR/ERK signalling pathway

Background

Many studies have demonstrated genetic and environmental factors that lead to renal cell carcinoma (RCC) and that occur during a protracted period of tumourigenesis. It appears suitable to identify and characterise potential molecular markers that appear during tumourigenesis and that might provide rapid and effective possibilities for the early detection of RCC. EGFR activation induces cell cycle progression, inhibition of apoptosis and angiogenesis, promotion of invasion/metastasis, and other tumour promoting activities. Over-expression of EGFR is thought to play an important role in tumour initiation and progression of RCC because up-regulation of EGFR has been associated with high grade cancers and a worse prognosis.

Methods

Characterisation of the protein profile interacting with EGFR was performed using the following: an immunohistochemical (IHC) study of EGFR, a comprehensive computational study of EGFR protein-protein interactions, an analysis correlating the expression levels of EGFR with other significant markers in the tumourigenicity of RCC, and finally, an analysis of the utility of EGFR for prognosis in a cohort of patients with renal cell carcinoma.

Results

The cases that showed a higher level of this protein fell within the clear cell histological subtype (p = 0.001). The EGFR significance statistic was found with respect to a worse prognosis. In vivo significant correlations were found with PDGFR-β, Flk-1, Hif1-α, proteins related to differentiation (such as DLL3 and DLL4 ligands), and certain metabolic proteins such as Glut5. In silico significant associations gave us a panel of 32 EGFR-interacting proteins (EIP) using the APID and STRING databases.

Conclusions

This work summarises the multifaceted role of EGFR in the pathology of RCC, and it identifies EIPs that could help to provide mechanistic explanations for the different behaviours observed in tumours.

Searching for Hif1-α interacting proteins in renal cell carcinoma

INTRODUCTION

Kidney tumours are frequently characterised by hypoxic conditions due to a local imbalance between oxygen (O2) supply and consumption. Hif1-α regulates angiogenesis, tumour growth, tumour progression, metastatic spread, and glucose metabolism by acting as a transcription factor for relevant genes. Here, we describe an immunohistochemical study of Hif1-α, a comprehensive computational study of Hif1-α interacting proteins (HIPs), an analysis correlating expression levels of Hif1-α with upstream and downstream proteins, and an analysis of the utility of Hif1-α for prognosis in a cohort of patients with renal cell carcinoma.

MATERIALS AND METHODS

The patient cohort included 80 patients. For immunohistochemistry evaluation, tissue microarrays were constructed. The IntAct, MINT, and BOND databases were used for the HIP approach. The Kruskal-Wallis test was used for comparing protein expression with pathology measurements. Correlation was expressed as the Pearson coefficient.

RESULTS

Hif1-α expression correlates significantly with the "clear" histological subtype of renal cell carcinoma (p < 0.01). The samples with the worst prognoses related to the pathological variables analysed showed the highest levels of Hif1-α expression. Significant correlations were found with Bcl-2, CAIX, C-kit, EGFR, TGF-β, proteins of the VEGF family, proteins related to differentiation (such as Notch1 and Notch3) and certain metabolic enzymes. Bioinformatic analysis suggested 45 evidence-based HIPs and 4 complexes involving protein Hif1-α.

CONCLUSIONS

This work summarises the multifaceted role of Hif1-α in the pathology of renal cell carcinomas, and it identifies HIPs that could help provide mechanistic explanations for the different behaviours seen in tumours.

Rewiring the dynamic interactome

Transcriptomics continues to provide ever-more evidence that in morphologically complex eukaryotes, each protein-coding genetic locus can give rise to multiple transcripts that differ in length, exon content and/or other sequence features. In humans, more than 60% of loci give rise to multiple transcripts in this way. Motifs that mediate protein-protein interactions can be present or absent in these transcripts. Analysis of protein interaction networks has been a valuable development in systems biology. Interactions are typically recorded for representative proteins or even genes, although exploratory transcriptomics has revealed great spatiotemporal diversity in the output of genes at both the transcript and protein-isoform levels. The increasing availability of high-resolution protein structures has made it possible to identify the domain-domain interactions that underpin many protein interactions. To explore the impact of transcript and isoform diversity we use full-length human cDNAs to interrogate the protein-coding transcriptional output of genes, identifying variation in the inclusion of protein interaction domains. We map these data to a set of high-quality protein interactions, and characterise the variation in network connectivity likely to result. We find strong evidence for altered interaction potential in nearly 20% of genes, suggesting that transcriptional variation can significantly rewire the human interactome.

Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression

The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.

Beyond captions: linking figures with abstract sentences in biomedical articles

Although figures in scientific articles have high information content and concisely communicate many key research findings, they are currently under utilized by literature search and retrieval systems. Many systems ignore figures, and those that do not typically only consider caption text. This study describes and evaluates a fully automated approach for associating figures in the body of a biomedical article with sentences in its abstract. We use supervised methods to learn probabilistic language models, hidden Markov models, and conditional random fields for predicting associations between abstract sentences and figures. Three kinds of evidence are used: text in abstract sentences and figures, relative positions of sentences and figures, and the patterns of sentence/figure associations across an article. Each information source is shown to have predictive value, and models that use all kinds of evidence are more accurate than models that do not. Our most accurate method has an -score of 69% on a cross-validation experiment, is competitive with the accuracy of human experts, has significantly better predictive accuracy than state-of-the-art methods and enables users to access figures associated with an abstract sentence with an average of 1.82 fewer mouse clicks. A user evaluation shows that human users find our system beneficial. The system is available at http://FigureItOut.askHERMES.org.

GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions

Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.

Genes2FANs: connecting genes through functional association networks

Background

Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent.

Results

Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories.

Conclusions

Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in many cancers are mostly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs, can guide better strategies for disease gene discovery. Genes2FANs is available at: http://actin.pharm.mssm.edu/genes2FANs.

Protein interaction data curation: the International Molecular Exchange (IMEx) consortium

The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.

A taxonomy of organ-specific breast cancer metastases based on a protein-protein interaction network

We carried out a systems-level study of the mechanisms underlying organ-specific metastases of breast cancer. We followed a network-based approach using microarray expression data from human breast cancer metastases to select organ-specific proteins that exert a range of functions allowing cell survival and growth in the microenvironment of distant organs. MinerProt, a home-made software application, was used to group organ-specific signatures of brain (1191 genes), bone (1623 genes), liver (977 genes) and lung (254 genes) metastases by function and select the most differentially expressed gene in each function. As a result, we obtained 19 functional representative proteins in brain, 23 in bone, 15 in liver and 9 in lung, with which we constructed four organ-specific protein-protein interaction networks. The network taxonomy included seven proteins that interacted in brain metastasis, which were mainly associated with signal transduction. Proteins related to immune response functions were bone specific, while those involved in proteolysis, signal transduction and hepatic glucose metabolism were found in liver metastasis. No experimental protein-protein interaction was found in lung metastasis; thus, computationally determined interactions were included in this network. Moreover, three of these selected genes (CXCL12, DSC2 and TFDP2) were associated with progression to specific organs when tested in an independent dataset. In conclusion, we present a network-based approach to filter information by selecting key protein functions as metastatic markers or therapeutic targets.

Detecting mutually exclusive interactions in protein-protein interaction maps

Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.

Templates are available to model nearly all complexes of structurally characterized proteins

Traditional approaches to protein-protein docking sample the binding modes with no regard to similar experimentally determined structures (templates) of protein-protein complexes. Emerging template-based docking approaches utilize such similar complexes to determine the docking predictions. The docking problem assumes the knowledge of the participating proteins' structures. Thus, it provides the possibility of aligning the structures of the proteins and the template complexes. The progress in the development of template-based docking and the vast experience in template-based modeling of individual proteins show that, generally, such approaches are more reliable than the free modeling. The key aspect of this modeling paradigm is the availability of the templates. The current common perception is that due to the difficulties in experimental structure determination of protein-protein complexes, the pool of docking templates is insignificant, and thus a broad application of template-based docking is possible only at some future time. The results of our large scale, systematic study show that, surprisingly, in spite of the limited number of protein-protein complexes in the Protein Data Bank, docking templates can be found for complexes representing almost all the known protein-protein interactions, provided the components themselves have a known structure or can be homology-built. About one-third of the templates are of good quality when they are compared to experimental structures in test sets extracted from the Protein Data Bank and would be useful starting points in modeling the complexes. This finding dramatically expands our ability to model protein interactions, and has far-reaching implications for the protein docking field in general.

From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases

Celiac disease is characterized by a chronic inflammatory reaction in the intestine and is triggered by gluten, a constituent derived from grains which is present in the common daily diet in the Western world. Despite decades of research, the mechanisms behind celiac disease etiology are still not fully understood, although it is clear that both genetic and environmental factors are involved. To improve the understanding of the disease, the genetic component has been extensively studied by genome-wide association studies. These have uncovered a wealth of information that still needs further investigation to clarify its importance. In this review, we summarize and discuss the results of the genetic studies in celiac disease, focusing on the "non-HLA" genes. We also present novel approaches to identifying the causal variants in complex susceptibility loci and disease mechanisms.

From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases

Celiac disease is characterized by a chronic inflammatory reaction in the intestine and is triggered by gluten, a constituent derived from grains which is present in the common daily diet in the Western world. Despite decades of research, the mechanisms behind celiac disease etiology are still not fully understood, although it is clear that both genetic and environmental factors are involved. To improve the understanding of the disease, the genetic component has been extensively studied by genome-wide association studies. These have uncovered a wealth of information that still needs further investigation to clarify its importance. In this review, we summarize and discuss the results of the genetic studies in celiac disease, focusing on the “non-HLA” genes. We also present novel approaches to identifying the causal variants in complex susceptibility loci and disease mechanisms.

Network-based identification of novel connections among apoptotic signaling pathways in cancer

MicroRNAs (miRNAs), highly conserved, non-coding endogenous RNA and nearly ~22 nucleotides (nt) in length, are well-known to regulate several apoptotic pathways in cancer. In this study, we computationally constructed the initial human apoptotic PPI network by several online databases, and further integrated these high-throughput datasets into a Naïve Bayesian model to predict protein functional connections. Based on the modified apoptotic network, we identified several apoptotic hub proteins such as TP53, SRC, M3K3/5/8, cyclin-dependent kinase2/6, TNFR16/19, and TGF-β receptor 1/2. Subsequently, we identified some microRNAs that could target the aforementioned apoptotic hub proteins by using TargetScan, PicTar, and Diana-MicroH. In conclusion, these results demonstrate the PPI network-based identification of new connections amongst apoptotic pathways in cancer, which may shed new light on the intricate relationships between core apoptotic pathways and some targeted miRNAs in human cancers.

Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs

BACKGROUND

Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed.

RESULTS

In this paper, we constructed an integrated M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework.

CONCLUSIONS

A protein interaction map of M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.

A comparative analysis of protein targets of withdrawn cardiovascular drugs in human and mouse

Background

Mouse is widely used in animal testing of cardiovascular disease. However, a large number of cardiovascular drugs that have been experimentally proved to work well on mouse were withdrawn because they caused adverse side effects in human.

Methods

In this study, we investigate whether binding patterns of withdrawn cardiovascular drugs are conserved between mouse and human through computational dockings and molecular dynamic simulations. In addition, we also measured the level of conservation of gene expression patterns of the drug targets and their interacting partners by analyzing the microarray data.

Results

The results show that target proteins of withdrawn cardiovascular drugs are functionally conserved between human and mouse. However, all the binding patterns of withdrawn drugs we retrieved show striking difference due to sequence divergence in drug-binding pocket, mainly through loss or gain of hydrogen bond donors and distinct drug-binding pockets. The binding affinities of withdrawn drugs to their receptors tend to be reduced from mouse to human. In contrast, the FDA-approved and best-selling drugs are little affected.

Conclusions

Our analysis suggests that sequence divergence in drug-binding pocket may be a reasonable explanation for the discrepancy of drug effects between animal models and human.

AtPAN: an integrated system for reconstructing transcriptional regulatory networks in Arabidopsis thaliana

Background

Construction of transcriptional regulatory networks (TRNs) is of priority concern in systems biology. Numerous high-throughput approaches, including microarray and next-generation sequencing, are extensively adopted to examine transcriptional expression patterns on the whole-genome scale; those data are helpful in reconstructing TRNs. Identifying transcription factor binding sites (TFBSs) in a gene promoter is the initial step in elucidating the transcriptional regulation mechanism. Since transcription factors usually co-regulate a common group of genes by forming regulatory modules with similar TFBSs. Therefore, the combinatorial interactions of transcription factors must be modeled to reconstruct the gene regulatory networks.

Description For systems biology applications, this work develops a novel database called Arabidopsis thaliana Promoter Analysis Net (AtPAN), capable of detecting TFBSs and their corresponding transcription factors (TFs) in a promoter or a set of promoters in Arabidopsis. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Additionally, proteins interacting with the co-expressed TFs are also incorporated to reconstruct co-expressed TRNs. Moreover, combinatorial TFs can be detected by the frequency of TFBSs co-occurrence in a group of gene promoters. In addition, TFBSs in the conserved regions between the two input sequences or homologous genes in Arabidopsis and rice are also provided in AtPAN. The output results also suggest conducting wet experiments in the future.

Conclusions

The AtPAN, which has a user-friendly input/output interface and provide graphical view of the TRNs. This novel and creative resource is freely available online at http://AtPAN.itps.ncku.edu.tw/.

Extending signaling pathways with protein-interaction networks. Application to apoptosis

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.

SCPC: a method to structurally compare protein complexes

MOTIVATION

Protein-protein interactions play vital functional roles in various biological phenomena. Physical contacts between proteins have been revealed using experimental approaches that have solved the structures of protein complexes at atomic resolution. To examine the huge number of protein complexes available in the Protein Data Bank, an efficient automated method that compares protein complexes is required.

RESULTS

We have developed Structural Comparison of Protein Complexes (SCPC), a novel method to structurally compare protein complexes. SCPC compares the spatial arrangements of subunits in a complex with those in another complex using secondary structure elements. Similar substructures are detected in two protein complexes and the similarity is scored. SCPC was applied to dimers, homo-oligomers and haemoglobins. SCPC properly estimated structural similarities between the dimers examined as well as an existing method, MM-align. Conserved substructures were detected in a homo-tetramer and a homo-hexamer composed of homologous proteins. Classification of quaternary structures of haemoglobins using SCPC was consistent with the conventional classification. The results demonstrate that SCPC is a valuable tool to investigate the structures of protein complexes.

AVAILABILITY

SCPC is available at http://idp1.force.cs.is.nagoya-u.ac.jp/scpc/.

CONTACT

rkoike@is.nagoya-u.ac.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A systems biology approach for detecting toxicity-related hotspots inside protein interaction networks

Drug-induced neutropenia can be fatal when severe and therefore requires an improved understanding of its mechanism(s) of toxicity. Systems biology provides an opportunity to understand adverse events after drug administration using analysis of biomolecular networks. In this study, a human protein interaction network was analyzed to identify proteins that are most central to topological paths connecting a drug's target proteins to hematopoiesis-related proteins. For a set of non-immune neutropenia inducing drugs, 9 proteins were found to be common to putative signaling paths across all drugs evaluated. All 9 proteins showed relevance to neutrophil biology. Geneset enrichment analysis showed that proteins associated with cancer-related processes such as apoptosis provide topological linkages between drug targets and proteins involved in neutrophil production. The algorithm can be applied towards analysis of any toxicity where the drugs and the physiological processes involved in the toxic mechanism are known.

IMID: integrated molecular interaction database

MOTIVATION

Molecular interaction information, such as protein-protein interactions and protein-small molecule interactions, is indispensable for understanding the mechanism of biological processes and discovering treatments for diseases. Many databases have been built by manual annotation of literature to organize such information into structured form. However, most databases focus on only one type of interactions, which are often not well annotated and integrated with related functional information.

RESULTS

In this study, we integrate molecular interaction information from literature by automatic information extraction and from manually annotated databases. We further integrate the relationships between protein/gene and other bio-entity terms including gene ontology terms, pathways, species and diseases to build an integrated molecular interaction database (IMID). Interactions can be selected by their associated probabilities. IMID allows complex and versatile queries for context-specific molecular interactions, which are not available currently in other molecular interaction databases.

AVAILABILITY

The database is located at www.integrativebiology.org.

Prediction and characterization of protein-protein interaction networks in swine

Background

Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Results

We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.

Conclusion

The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).