The molecular characteristics of human diseases are often represented by a list of genes termed “signature genes”. A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution. Diseases are abnormal conditions of the human body resulted from significant nonlethal malfunctions that affect the human physiological system. Traditionally, diseases are characterized by pathology and observation of clinical phenotypes. Although these methods have proved successful in many applications, they lack the sensitivity to detect diseases before the appearance of symptoms and also have a limited ability to distinguish complex disease classes ,2 which With the development of genomic technology, a promising approach to overcome limitations of the traditional method is to identify a set of genes as a genetic signature whose combined expression pattern is the uniquely characteristic of a given phenotype ,4. In thThere are still obvious obstacles that prevent the application of gene signatures in clinical practice ,13. One Pathway-based methods have been extensively applied to analyze large-scale genome-wide data with varied purposes and applications -29. SomeIn this study, we report a new approach to characterize diseases at the functional level, with our aim being to both consolidate redundant gene lists and to generate a list of pathways which is both accurate and reproducible. For a given disease, a pathway profile is generated based on the enrichment analysis of differential gene expression data between normal and tumor tissues: a binary vector whose elements indicate whether a given function (represented by a KEGG pathway) is perturbed. Using breast cancer and four clinically-relevant subdivisions  as examples, we examine the new approach from three perspectives: to determine whether the pathway profile can be reproduced from the data generated by different technologies (Microarray vs. RNA-Seq), as well as from separate cohorts (The Cancer Genome Atlas (TCGA) vs. The European Genome-phenome Archive (EGA)), to determine whether the resulting pathways are associated with the functional perturbation resulted from the breast cancer and its subtypes, and finally to determine whether the pathway profile can distinguish different subtypes of breast cancer as well as distinguish breast cancer from other diseases. Our results indicate that the new approach achieves 68% average reproducibility between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers by extensive literature search. Approximately 40% of the pathways are enriched in all four subtypes and there are 4, 2, 4, and 7 pathways enriched only in the luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively, implying that pathway profiles not only reveal shared mechanisms in the four subtypes but also outline the subtype-specific operations that may potentially be used as signature pathways to distinguish them. Comparison of profiles between subtypes, as well as other diseases including ovarian cancer, glioblastoma multiforme (GBM), and obesity, reveals that the luminal A and luminal B subtypes of breast cancer are closer to each other than to other subtypes, luminal A and luminal B subtypes are closer to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.808 tumor and 106 normal samples of TCGA RNA-Seq data (Illumina HiSeq 2000 RNA Sequencing platform) were downloaded from the TGGA portal on Oct. 2012. 522 tumor samples with available PAM50 classification  and 63 nhttp://bioinformatics.mdanderson.org/main/TCGABatchEffects:Overview).No major batch effects were observed for the two TCGA breast cancer data sets . The batA total of 269 human pathways were downloaded from KEGG  on Jun. To be consistent with clinical practice, we classified tumor samples into luminal A, luminal B, triple-negative, and HER2+ subtypes using the following steps. First, HER2+ samples were identified based on the test results of immunohistochemistry (IHC) or florescence in situ hybridization (FISH). The rest of the samples were then split into ER+ and ER- samples based on estrogen receptor (ER) status provided by IHC test results. ER+ samples were then further classified into luminal A and luminal B subtypes using PAM50 classification. Finally, triple-negative samples were extracted from the pool of ER- samples according to progesterone receptor (PR) status based on IHC test results. PAM50 classification results alone were used whenever ER or HER2 status was not available in the clinic data. As EGA data does not provide the PR status, instead we used the expression-based classification result for PR status . PAM50 cWe focused on the enrichment of pathways abnormally perturbed in the disease state compared to the normal state in four major subtypes of breast cancer. PWEA  was usedr can be defined asOur measure of reproducibility was applied to both pathway and gene-based profiles. Using pathways as an example, the reproducibility N and N’ are the number of enriched pathways for two different data sets, and Nc is the number of overlapping of enriched pathways between two data sets.where For a given disease, a pathway profile p can be defined as:N is the total number of pathways used in the analysis and pn (1 ≤ n ≤ N) is equal to either 1 or 0 to indicate whether nth pathway is enriched or not, respectively.where The sketch of our approach is depicted in Figure Breast cancer is a complex and heterogeneous disease  which coThe numbers of enriched pathways for each subtype in different data sets are shown in Table Reproducibility is an essential requirement for almost all published scientific work. We focused on the reproducibility of our method over varying patient cohorts and platforms, with the intention to show that our result can be broadly applied in practice.The reproducibility (See definition in Methods) of our method was evaluated using the gene expression data generated either from different expression measurement technology (Microarray vs. RNA-Seq), different sample sets (TCGA vs. EGA), or both. Only matched tumor samples (496) and normal samples (58) from each TCGA data set were used when examining the reproducibility between two technologies, and all 914 TCGA RNA-Seq samples  and 585 TCGA microarray samples  were used for the rest of the analysis.Overall, about 67%, 71%, 67% and 76% average reproducibility were achieved Figure  for lumiRecently, RNA-Seq has become a powerful alternative to microarrays due to advantages such as high resolution, increased dynamic range, lower background noise, relatively little technical variation, and the ability to profile the entire transcriptome ,48. It iWe focus on the matched samples between the TCGA RNA-Seq and TCGA microarray data sets to examine the effect of gene expression measurement technology on the reproducibility of our approach. The comparison of enriched pathways generated from the two data sets results in 60%, 73%, 65% and 70% reproducibility for the luminal A, luminal B, triple-negative, and HER2+ subtypes respectively, with about 67% on average. We also found that the reproducibility based on matched samples was slightly smaller than those based on all samples except in the case of luminal B: 65%, 70%, 66%, and 75% respectively, for luminal A, luminal B, triple-negative and HER2+ subtypes as shown as blue bars in the left side of Figure We further investigate the reproducibility of our approach across different data sets. Among the three data sets used in the study, there is, as expected, the smallest difference between TCGA RNA-Seq and TCGA microarray data sets because 496 out of 522 samples from the TCGA microarray data are drawn from the same patient pool as the TCGA RNA-Seq data set, whereas the largest difference separated the TCGA RNA-Seq and EGA microarray data (different patients and different technologies). However, the corresponding average reproducibility over four subtypes is almost invariable with 69%, 68%, and 74% for the data set pairs TCGA RNA-Seq vs. TCGA microarray, TCGA microarray vs. EGA microarray, and TCGA RNA-Seq vs. EGA microarray respectively  are known to be associated with breast cancer or cancers such as those shown in Table As shown in Figure Among 28 CEPs, 9 are relevant to metabolism such as Glycolysis/gluconeogenesis, Purine metabolism and Tyrosine metabolism. This is not surprising, as cancer cells must perturb metabolic pathways to provide energy and building blocks to support aggressive cell growth and proliferation . Thus, tWe also noted that CEPs consist of some well-known cancer related pathways such as pathways in the categories of replication and repair, signal transduction, cell growth and death. For example, the CEP “Cell cycle” appears to be in agreement with the intuition that inappropriate proliferation is one of the most remarkable characteristics of the cancer cell. This is expected and can be considered as a benchmark result for our method, as several other pathway-based studies in cancer have observed similar results -53. AddiTwo hormonally-related pathways are observed to be enriched in CEPs as well, including Progesterone-mediated oocyte maturation and the Estrogen signaling pathway. As expected, we observe that Estrogen signaling is significantly upregulated in ER+ tumors  and significantly downregulated in triple-negative tumors. Though HER2+ tumors are not known to be directly enriched in estrogen-related genes, nevertheless we find that the pathway is still significantly dysregulated in HER2+ tumors. It has been suggested that ER+/HER2+ tumors utilize crosstalk between the MAPK signaling pathway and the ER signaling pathway to evade common anti-estrogenic therapies such as Tamoxifen .Subtype-specific pathways are especially interesting, since they are potential candidates for signature pathways for each subtype of breast cancer. As shown in Figure Luminal A has 4 subtype-specific pathways: Ubiquitin mediated proteolysis, Endocytosis, Carbohydrate digestion and absorption, and Vasopressin-regulated water reabsorption. The enrichment of Ubiquitin mediated proteolysis pathway is evidenced by the recent work  where itBRCA2 gene which is known to be involved in error-free DNA repair of double-strand breaks (DSBs) through homologous recombination and BRCA2 mutation carriers have a predilection for developing breast cancers of the luminal B subtype [There are two interesting pathways that are not enriched in luminal A subtype in comparison to the other three subtypes: the Homologous recombination and the p53 signaling pathways. This observation is consistent among all three data sets and may therefore be used to aid in the distinguishability of the luminal A subtype. This observation is also supported in the literature where it has been reported that a functional defect in homologous recombination is common in triple-negative breast cancer and in a subset of high grade ER and/or HER2 positive breast cancer . Homolog subtype . On the  subtype , therefoThe luminal B subtype has only two subtype-specific pathways: Histidine metabolism and Phosphatidylinositol signaling systems, although it has many more enriched pathways than the luminal A subtype. It should also be noted that pathways enriched by the luminal B subtype have much more overlaps with those found enriched in the triple-negative and HER2+ subtypes than in the luminal A subtype Figure .Since ER status is a very important factor in planning breast cancer treatment, we also outline the pathways that are specific to the ER+ subtype . Among 6 ER+ specific pathways, four of them have supporting evidence from previous studies: the Primary bile acid biosynthesis, Jak-STAT signaling pathway, Complement and coagulation cascades, and GnRH signaling pathways  has been reported as an oncogene in the progression of triple negative breast cancer , whose aThe HER2+ subtype has the largest number of subtype-specific pathways among the four subtypes as shown in Table The resolution of our approach aims to test whether the pathway profiles have enough detail not only to distinguish the different diseases but also to correctly assess the correlations between them based on the perturbed functions. To achieve this, we evaluate the disease correlation by calculating the hypergeometric probability of corresponding pathway profiles: a method that has been successfully applied to calculate the correlation between phylogenetic profiles in our previous study . We furtp-value cutoff, a total of 17 disease pairs exhibit significant correlation with varied strength (See Additional file The resulting profiles are drawn in Figure All the enriched pathways in this study are identified based on a FDR cut-off 0.1. This cut-off was optimized to achieve reasonable reproducibility Figure  while maThe comparison of the reproducibility resulted from the pathway profiles and top DEGs are in general conservative. From a hypergeometric statistics perspective, a larger number of genes resulted from the different analyses will achieve better reproducibility. We therefore used the top 1500 DEGs in the reproducibility comparison instead of known signature genes available in the literature because the number of the latter is generally much smaller than 1500. In addition, we also performed the comparison using the top 6000 DEGs and the enriched pathways still achieves better reproducibility (See details in Additional file p-values or enrichment scores of given pathways.Despite the improvement over classical gene-based methods in characterizing breast cancer and related diseases, this approach has great potential to be further enhanced. First, it depends on the prior knowledge of pathways which are far from complete, and identifying new pathways is a difficult, time-consuming, and labor intensive work. An alternative approach to partially ameliorate this limit is to replace pathways with functional modules that may be computationally identified . Second,Reproducibility is one of the main challenges for the identification of gene signatures. Besides technical factors, the disparity may result from the fact that diseases are directly caused by the perturbations of the molecular function that are generally carried out by a set of genes in the form of modules or pathways. Therefore it may be more appropriate to characterize diseases at the functional level than at the gene level. Following this perspective, we developed a novel approach to characterize diseases using the pathway profiles and evaluated the approach based on profiles’ reproducibility, accuracy and resolution. Using four subtypes of breast cancer as an example, the results of this new approach are promising with 70% average reproducibility, 74% average accuracy (e.g., references in Table TCGA: The Cancer Genome Atlas; EGA: The European Genome-phenome Archive; GBM: Glioblastoma multiforme; DEGs: Differentially expressed genes; FDR: False discovery rate; PPs: Pathway profiles; RPPs: Reference pathway profiles; CEP: Common enriched pathway.The authors declare that they have no competing interests.FT and YW designed the method and experiments. FT carried out the computation and both FT and YW performed the analysis. FT drafted the manuscript. MS normalized TCGA breast cancer RNA-Seq data, provided constructive advice, and revised the manuscript. ZH supervised the project, gave suggestions, and revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/7/45/prepubSupplementary materials on determination of ER, PR, and HER2 status, differentially expressed genes, determination of FDR cut-off, coverage of KEGG pathway genes, comparison between consistent and inconsistent pathways, EGA validation data set, and ER+ specific pathways.Click here for fileLists of samples for three data sets and four subtypes of breast cancer.Click here for filePWEA results of all used data sets.Click here for fileLists of consistent and inconsistent pathways across three data sets for four subtypes of breast cancer.Click here for fileLists of CEPs and subtype specific pathways.Click here for fileFDR of enriched pathways for pathway profiles shown in Figure 4A.Click here for filep-values for subtype/disease correlations shown in Figure 4B.Click here for file


Recently, biology has become a data intensive science because of huge datasets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. In molecular biology, the list of components at the genome, transcriptome, proteome, and metabolome levels is gradually becoming complete and well-known to scientists. However, it is not holistically known how these components interact with each other to grow and maintain and reproduce life at different phases, in different environments, or with different challenging conditions. Networks at the molecular level are constructed to understand and explain processes and subprocesses of the cell. New tools and algorithms are being continuously developed for the purpose of handling and mining big biological data and networks aiming to serve humanity by developing smart health care systems, new generation medical tests, drugs, foods, fuel, materials, sensors, and so on. Overall, this improves the understanding of the cell or in other words the life as a system. Therefore, the range of topics under big data and network biology is extensive and the present special issue is not a comprehensive representation of the subject. Nonetheless, the articles selected for this special issue represent versatile topics concerning the title that we have the pleasure of sharing with the readers.The review paper “A Glimpse to Background and Characteristics of Major Molecular Biological Networks” focuses on biological background and topological properties of gene regulatory, transcriptional regulatory, protein-protein interaction, and metabolic and signaling networks. Versatile information contained in this article is helpful to facilitate a comprehensive understanding and to conceptualize the foundation of network biology.The paper titled “METSP: A Maximum-Entropy Classifier Based Text Mining Tool for Transporter-Substrate Identification with Semistructured Text” discusses a method for identifying transporter-substrate pairs by text mining and applied it to human transporter annotation sentences collected from UniProt database. The substrates of a transporter are not only useful for inferring function of the transporter, but also important in discovery of compound-compound interactions and reconstruction of metabolic pathways.Volatile organic compounds (VOCs) play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. The paper titled “Development and Mining of a Volatile Organic Compound Database” discusses creation of a new VOC database by collecting information scattered in scientific literature and analyzed the accumulated data to show relations between biological functions and chemical structures of VOCs. This work also shows that VOC based classification of microorganisms is consistent with their classification based on pathogenicity.When inconsistent policies are applied to hospital computer systems, it can produce enormous problems, such as stolen information, frequent failures, and loss of the entire or part of the hospital data. The paper “EMRlog Method for Computer Security for Electronic Medical Records with Logic and Data Mining” presents a new method named EMRlog for computer security systems in hospitals based on two kinds of policies, that is, directive and implemented policies. Treponema pallidum” focuses on identifying critical reactions by analyzing the topological structure of the metabolic network. Treponema pallidum is the syphilis-causing pathogen and the critical reactions of its metabolism are important drug targets and such information can lead to invention of effective vaccine of syphilis.The paper “Cellular Metabolic Network Analysis: Discovering Important Reactions inThe paper “Systematic Analysis of the Associations between Adverse Drug Reactions and Pathways” focuses on identifying relations between adverse drug reactions (ADRs) and biological pathways by integrating clinical phenotypic data, biological pathway data, and drug-target relations. This work suggests that drug perturbation in a certain pathway can cause changes in multiple organs, rather than in a specific organ.The paper “Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks” proposes a new graph theory based method to identify distinct functional modules associated with specific cancer types. The method was applied to nine different cancer PPI networks. The distinct modules identified by this work have a high correlation with those found in the experimental datasets related to specific cancer types.The paper “Shaped 3D Singular Spectrum Analysis for Quantifying Gene Expression, with Application to the Early Zebrafish Embryo” presents a new approach for studying gene expression in nuclei located in a thick layer around a spherical surface by integrating several methods such as depth equalization on the sphere, flattening, interpolation to a regular grid, pattern extraction by shaped 3D singular spectrum analysis (SSA), and interpolation back to original nuclear positions.The paper titled “Analysis of Chemical Properties of Edible and Medicinal Ginger by Metabolomics Approach” has investigated the chemical properties of medicinal and edible ginger cultivars using a liquid-chromatography mass spectrometry (LC-MS) approach. This work suggests the importance of acetylated derivatives of gingerol as medicinal indicators.The paper titled “Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data” proposes a new and faster variant of self-organizing map (SOM) algorithm for comprehensive analysis of big sequence data without the use of high-performance super computers. The performance of the proposed method has been verified by applying it to bacterial genome sequences. The new approach has been able to cluster the sequences according to phylotype with high accuracy.The paper “An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species” proposes a supervised Pairwise Ortholog Detection (POD) approach by combining a set of gene pair features based on similarity measures, such as alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles. The performance of the proposed method has been compared with several existing methods in the context of three pairs of yeast genomes.


J Comput Biol 19:563–573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI).Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes  pathway diagrams marked-up to highlight impacted genes.The PRS tool provides a filter in the isolation of biologically-relevant insights from complex transcriptomic data.The online version of this article (doi:10.1186/s12859-014-0358-2) contains supplementary material, which is available to authorized users. Increasingly, high-throughput transcriptional profiling techniques  inform modern life-science research. Such techniques provide a molecular “camera” taking genome-wide “snap-shots” of genetic activity. However, the effective analysis of microarray data presents a number of challenges, in particular handling the large number of genes that are studied simultaneously.et al [i.Over-representation Analysis (ORA): This scores a pathway by considering the proportion of differentially-expressed genes (DEGs) observed in each pathway relative to the proportion of all microarray DEGs. This is used by several pathway analysis tools, including GenMAPP , GoMinerii.Functional Class Scoring (FCS): FCS gives a score to each gene in a pathway based on its expression, from which a pathway-score is calculated based on the scores of all the genes in the pathway. A number of FCS methods have been implemented through standalone tools such as GSEA , SigPathiii.Pathway Topology (PT)-based approaches: These approaches exploit the topology of pathways by giving weights to pre-defined connections between genes, which inform pathway scoring. Several topology-based approaches have been described in the literature over the past few years. According to Mitrea et al , PT-baseAnalysing gene expression in the context of curated knowledge, or “knowledge base-driven pathway analysis”, is critical as this guides the reduction in search space from many thousands of genes to an subset of biological processes, which are much more tractable to human interpretation . Accordiet al , pathwayPreviously, we proposed a new pathway enrichment method, in which both pathway topology and the magnitude of gene expression changes informed the creation of a Pathway Regulation Score (PRS) . SpecifiHerein, we described the implementation of our PRS approach as a standalone tool that provides end users with the option of importing data from different microarray platforms and species. The tool yields both PRS and z-scores, provides statistical analysis, and allows browsing of pathways with impacted genes highlighted in different colours. An enhancement from our original report is that users are able to enrich both signalling and metabolic pathways.The PRS approach was implemented in MATLAB. Users without access to the MATLAB environment can down-load the MATLAB Runtime Compiler (MRC) in order to deploy the software described herein, via a user-friendly GUI. The PRS interface Figure  providest-test thresholds, normalisation method and platform is required.We did not re-engineer a filter to normalise data from a variety of platforms, rather users must first preprocess transcriptomic data using one of the myriad existing tools. Data must be in the form of a simple Excel spreadsheet, in which the first column should be probe ID, and the following columns normalised replicated expression values from the control and test conditions. Additional information regarding species, sample numbers, fold-change and Our fundamental algorithm was described previously . BrieflyRepresenting pathways as graphs had an additional advantage as it reduced redundancy in that genes were only represented once in any pathway graph. A Depth-First Search (DFS) algorithm, derived from Graph Theory was used to ensure that loops were only counted once.Our method assigned weights to all significant nodes (i.e. DEGs) in a pathway to reflect their topological strength . A PRS n times, where n is provided by the user through the interface . The statistical significance  of each pathway score was estimated by a comparison between the observed score and the n random scores generated. To achieve more reliable statistical significance evaluation, p-values were adjusted for multiple-test correction by a False Discovery Rate (FDR) method based on a threshold provided by the user. This is described in more detail in our original report [We then went on to establish the probability of achieving scores at least as high as the PRS score by chance using a non-parametric permutation method. Initially, fold-change values for all expressed microarray genes were permuted. These values were then mapped back onto pathways, and a PRS recalculated. This process was repeated l report .After running the analysis, results are saved as .mat format files for ease of retrieval. By clicking on the pathway name from the list of ranked pathways shown in the table and selecting the option of visualizing a pathway from the interface, a marked-up pathway map will be displayed. Technically, the software will call a pathway mapping web service (REST-based API service) hosted on the KEGG website and pass a number of parameters, including a list of all expressed genes with their fold- changes and specified colours to differentiate DEGs from non-impacted genes. Figure ▪MicroarrayObject: an object of the class “Microarray_Dataset” built by calling initialiseMicroarray function  to describe, model and visualize the structure and functions of our method by diagrams. There are 14 types of diagrams classified in three categories in UML 2.0 , howeveri.Conversion of pathways into graphs by the convertPath2Graph function, which requires the usage of kgmlObject that holds a list of entries, relations and reactions of all pathways.ii.Using information stored in kgmlObject and PathsList for each graph  is assigned to each weighted graph using the weights of the significant nodes in the graph and other parameters.Sequence diagrams were used to represent the functions of the PRS tool according to different types of interactions between objects. As an example, Figure We implemented all these classes, functions, and DFS algorithm using MATLAB R2010a.www.ncbi.nlm.nih.gov/geo; accession #GDS4337). We created an input file containing Affymetrix probe IDs and normalized gene expression data for each of the 63 samples. Other parameters required were sample numbers in each group , and fold-change and p-value threshold values to filter significant genes . Fold-change thres-holds are arbitrary, and the value selected in this example yielded a sufficient number of impacted genes to allow pathway mapping . The user can opt to enrich for signalling or metabolic pathways, or both (as in this example). Additional statistical testing can be performed, if required, by our permutation method . Tables The objective evaluation of novel enrichment analysis methods is difficult, relying on their ability to discern biological processes already known to be perturbed in disease states. We and others previously attempted this by studying performance across a range of datasets derived from distinct conditions : Platform independent.Programming language: MATLAB.Other requirements: MATLAB 2010a or higher. If MATLAB is not installed on your PC, you need to install the MCR (Matlab Compiler Runtime) environment first and then run the PRS tool.Restrictions for use: None.


Shoot branching patterns result from the spatio-temporal regulation of axillary bud outgrowth. Numerous endogenous, developmental and environmental factors are integrated at the bud and plant levels to determine numbers of growing shoots. Multiple pathways that converge to common integrators are most probably involved. We propose several pathways involving not only the classical hormones auxin, cytokinins and strigolactones, but also other signals with a strong influence on shoot branching such as gibberellins, sugars or molecular actors of plant phase transition. We also deal with recent findings about the molecular mechanisms and the pathway involved in the response to shade as an example of an environmental signal controlling branching. We propose the TEOSINTE BRANCHED1, CYCLOIDEA, PCF transcription factor TB1/BRC1 and the polar auxin transport stream in the stem as possible integrators of these pathways. We finally discuss how modeling can help to represent this highly dynamic system by articulating knowledges and hypothesis and calculating the phenotype properties they imply. The pattern of shoot branching, a major component of plant architecture, results from a complex spatio-temporal regulation of axillary bud outgrowth. Axillary meristems initiated at the axils of most leaves initiate a few leaves to form an axillary bud. These buds can undergo immediate growth and turn into a lateral branch or become dormant. Dormancy is not definitive: the bud can often resume its growth, e.g., in case of damage to the apex or at flowering initiation . At the Vicia faba was able to inhibit this outgrowth and auxin was the first hormone suggested to play a key role in the apical dominance process. Auxin mainly originates from the shoot apex and does not enter buds to inhibit their outgrowth, so it was hypothesized to inhibit bud outgrowth indirectly ], Arabidopsis [more axillary growth (max)], rice [dwarf (d)], and petunia [decreased of apical dominance (dad)]. They have led to the isolation of the strigolactone (SL)-deficient and SL-response mutants.  and GA (le) deficient, are more branched than single mutants (RAMOSUS6 (RMS6) and RMS7 genes. The rms6 and rms7 mutants only display increased basal branching, including at cotyledon nodes for rms6. The additive branching phenotype of the double mutants  suggests that they may play a part in different pathways and are SL-independent (per node) differ according to the gene involved. This could reflect the presence of independent regulatory networks.Nevertheless, SL mutants keep responding to environmental signals such as the photoperiod or planting density, or to removal of the shoot apex. Therefore other pathways than SLs’ influence shoot branching . Similar 3 EFL3; ] have ty 3 EFL3; . Dwarfis 3 EFL3; . In most mutants . Similarependent . InteresIn this review, recent advances in our understanding of how endogenous, environmental and developmental pathways control axillary bud outgrowth will be presented. Among the numerous environmental factors that can influence shoot branching, we focused on shade, an interesting and well-studied example of how light regulates the fate of axillary buds. These pathways may converge to common targets, so we propose key integrators of branching pathways. Last of all, we present how modeling/system biology can help to better understand and integrate these pathways.Two models, involving either signals acting downstream of auxin or the “auxin canalization” process, may explain the indirect role of auxin in the control of apical dominance . These mIn the classical model, auxin controls the level of a root-to-shoot moving signal that enters axillary buds and regulates their outgrowth . The auxIPT genes are modified in response to auxin levels. Repression of CK biosynthesis genes by auxin is commonly known  application, girdling, defoliation…; FINE CULM1/PsBRANCHED1 (FC1/PsBRC1) gene specifically expressed in axillary buds  was suggested . Once exin shoot . At the in shoot .At the plant phenotype level, this effect of SLs on PAT is characterized by increased competition among branches. This competition between shoot apices to export their auxin into the main PATS may also explain the process of correlative inhibition which has been largely studied using two-shoot plants: when a dominating, actively growing shoot reduces or inhibits the growth of a dominated shoot, the dominant branch has a higher capacity to transport labeled auxin and exports more endogenous IAA out of its apex than the dominated shoot. If the dominant branch is decapitated, growth rapidly resumes in the dominated branch, together with a strong increase in its capacity to transport IAA . It appePsbrc1 mutant only has one long basal branch and very short aerial branches at the upper node level whereas rms mutants have one or two branches at most nodes (Psbrc1 could be explained by the combination of both SL effects in the SL-deficient mutant rms1 (low levels of PsBRC1 transcripts in axillary buds and PAT affected). It would be of great interest to confirm whether the Psbrc1 mutant has a normal PAT or not.The two processes that may explain the indirect role of auxin in shoot branching  have been discussed and debated in several reviews . They arst nodes . This hiArabidopsis , a positive regulator of the brassinosteroid (BR) signaling pathway  starch reserve mobilization in stem tissues, mostly in perennial plants , (ii) hiactivity  that creransport . Paralleransport  and in xransport  is increransport  or sucroe supply . Still, –1) compared to auxin (1 cm h–1). The timing of axillary bud outgrowth matched well with their supply with phloem-transported photoassimilates; artificially increased sucrose levels promoted bud outgrowth in non-decapitated plants. Artificially applied sucrose down-regulated BRC1 expression within the first 2 h of incubation in pea  promoted bud outgrowth and expression of RhVI1, Rosa hybrida vacuolar invertase  availability and transduce the sugar signal. Preliminary data in isolated nodes of nvertase . We can nvertase  cannot rLight as an energy source and a signal is a major environmental factor controlling branching, as recently reviewed by . DiffereIn a canopy, capture of part of the incident light spectrum by plant green tissues reduces light intensity, R/FRs and blue light (B) intensity in the light transmitted further down . AccordiPhytochrome B (PHYB) plays a major role in the sensing of R/FR in plants. Inactivation of the active Pfr form of PHYB by high FR, as in shade conditions, triggers SAS, and branching inhibition ensues . The conTB1 homolog in sorghum  partially reduced their branching , while branching will still be promoted when R/FR alone is low  and FRIGIDA (FRI), two floral repressors in the vernalization pathway  or endogenous (plant growth regulators) factors, suggesting common regulatory pathways between the two processes. Late-flowering mutants often exhibit modified branching patterns. In  pathway , also re pathway . In foraolerance . Furtheransition .FT (FLOWERING LOCUS T)/TFL1 (TERMINAL FLOWER1) gene family is involved in the control of floral induction, but also in plant architecture through the control of determinate and indeterminate growth  proteins interact with BRC1. The brc1-2 mutant is highly branched, and its lateral branches flower earlier (A mode of action was recently proposed in  earlier . BRC1 miSPL gene family (10 out of 16 in Arabidopsis) are post-transcriptionally controlled by miR156  transcription factors control different aspects of plant development such as phase transitions , leaf development and plant maturation . Differey miR156 . Overexpbidopsis . Double ranching . Interesaffected . This hycontents .Corngrass is caused by the overexpression of two tandem miR156 genes. Among others, the mutant displays a high tillering phenotype and a short stature. Seven of the 13 potential SPL targets of miR156 were strongly down-regulated in the Corngrass mutant. In rice, accumulation of the SPL9/15 homolog, OsSPL14 or IDEAL PLANT ARCHITECTURE1 (IPA1) led to plants with fewer tillers, stronger productive stems, increased lodging resistance and increased yield. These results suggest that SPL proteins are branching inhibitors  and GA-response  mutants of Arabidopsis was reduced in inflorescence stems, with a sharp reduction in the abundance of PIN1 proteins compared to the wild type, but no change in PIN polarity  or the pattern of leaf vascularization. PAT in the main stem is also an important component of the control of shoot branching possibly by preventing the establishment of PAT out of axillary buds. Research on PAT focuses on the behavior of PIN proteins in cells to understand how they can influence an overall process at the plant level . SLs, bypolarity .As described above, the regulatory network of shoot branching involves multiple players of various types  that interact with feedback loops at both bud and plant scale. This complexity is made even more intricate by the dynamics of the system related to plant development and the fluctuation of environmental variables. The functioning of complex and dynamic systems cannot be inferred by experiments alone, which provide only a picture of regulatory networks in particular situations and at particular times. One approach consists in combining experiments with modeling, which offers the possibility to position and link the multiple and heterogeneous players logically. In such an approach, knowledge and hypotheses about the branching regulatory network can be gathered into a model and the hypothetical part can be assessed by thorough comparisons between the behavior of the modeled system and actual plant behavior .RMS gene network. By modeling auxin transport canalization Arabidopsis plants. By calibrating auxin transport models on experimental data, Modeling can be combined to experiments to provide insights into branching regulation. By using modeling in parallel with stepwise laboratory research The above-mentioned modeling studies each focused on understanding the role of one player of bud outgrowth . Integrating the role of the various players into a single scheme remains a challenge. We propose the following representation of the branching regulatory system. Branching pattern is the result of the outgrowth dynamics of the different buds. Outgrowth timing of one specific bud depends on the dynamics of hormones, nutrients such as sugars, and possibly light signals locally perceived by the bud. Those dynamics are closely dependent on bud position in the plant , on the in vitro. This makes it possible to control bud local conditions and get rid of the dependence between buds and the rest of the plant  in bud regulation and the role played by sugars has recently been evidenced. All these players interact with one another . However, much work is still needed to understand how the different players interact and control bud outgrowth. Interesting questions will be to distinguish between the trophic and signaling roles of sugar in bud outgrowth control, as well as distinguishing between bud outgrowth triggering and the subsequent bud elongation phase, which may involve different processes . To invehe plant . In thisTo assess how plant growth and environment affect the levels of the various players near each bud of a given plant, one approach can be to develop a functional–structural plant model. This modeling approach consists in representing plant physiological functioning in a realistic plant botanical structure . Each orTo estimate the amount of light intercepted by each organ, structural plant models are interfaced with light models. Different light models exist in the literature, with different accuracy levels . The morFinally, several more or less detailed models are available in the literature to model the temporal dynamics of nitrogen and carbon compounds . In all In this paper, we highlighted the complex control of axillary bud outgrowth and of shoot branching and the interplay of several pathways in its control. Parts of the regulatory pathways have been identified, however, current knowledge does not provide a unified view of how environmental and developmental signals, as well as hormones, and nutrients controls are integrated to determine the branching pattern of a plant. The Guest Associate Editor Alexandra Jullien declares that, despite being affiliated at the same institution as the authors Catherine Rameau and Bruno Andrieu, the review process was handled objectively and no conflict of interest exists. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. Modern systems biology holds a significant promise to accelerate the development of personalized drugs, namely, tailor-made pharmaceuticals adapted to each person's own genetic makeup. Consequently, it helps transform symptom-based disease diagnosis and treatment to “personalized medicine,” in which effective therapies are selected and optimized for individual patients . This prHigh-throughput DNA sequencing, such as Next-Generation Sequencing (NGS) –8, is unOne of the major challenges in NGS analytics is a reliable proteome-wide function inference of gene products. This is traditionally accomplished using sequence-based methods, which annotate target proteins by transferring molecular function directly from homologous sequences , 11. DesThe last decade has seen a growing interest in using distributed cyberinfrastructure (DCI) for various bioinformatics applications –21. For In this work, we present a pilot-based implementation of metathreading for the structural and functional characterization of large datasets of gene products. Specifically, we developed a pipeline for eThread, a recently developed metathreading approach , tailoreThis paper is organized as follows. Introductions of eThread, Amazon EC2, and SAGA-based pilot framework are presented. Then, our pipeline is described along with key strategies for parallelization. Benchmark results revealing characteristics of computational tasks required for the eThread pipeline as well as those associated with EC2 infrastructure are presented. We, then, discuss our contributions and future directions, which are followed by concluding remarks.As schematically shown in eThread is a metathreading tool and was developed for predicting protein structure and function, whose input is a protein sequence , 27. UnlDue to nature of the key strategy, that is, metathreading, eThread is a complicated tool requiring the execution of the 10 different threading tools that also could contain other tools and have dependency for the template library. Note that these tools were developed by different developers and their implementation strategies are all different and heterogeneous, which challenges an efficient execution of eThread. Previously, profiling of the individual tools using a single system was reported , and herThe basic structure of eThread is further illustrated with the simplest serial algorithm in Amazon provides the EC2 cloud computing environment which is an IaaS cloud . This inFor the eThread pipeline development, we chose to build one AMI configured for each single threading tool along with other additional programs needed, but it is also possible to install multiple tools in a single AMI. EC2 provides multiple types of instances and an end user is charged depending upon types of instances, running times, and storage options. Instance types are different in types of cores, the number of cores, memory sizes, instance storage, and network performance. The instances we used for this study are summarized in An efficient utilization of distributed cyberinfrastructure is essential for a distributed application such as our eThread pipeline . SAGA-PiWith Amazon EC2 infrastructure, a current SAGA-Pilot implementation handles the data management between tasks and S3 is configured to be used for the data store as default. In other words, any task once completed deposits predefined output into S3 storage and the next task is able to locate the output as its input.Application workload management is also provided by Pilot APIs as follows. Pilot APIs comprise compute-unit and data-unit classes as primary abstraction. Using these, a distributed application can specify a computational task with input and output files , 32. OncFor all benchmarking experiments, manually curated 110 protein gene sequences whose lengths are distributed between 51 and 600 aa (amino acids) are prepared see . These 1The schematic workflow of our developed pipeline is shown in The main script of the pipeline, located in a local machine owned by a user, starts with an initialization of Pilot service instances, each of whom manages an EC2 VM creation, job submissions to the Pilot instance(s), and data transfer if needed. Importantly, SAGA-Pilot allows the pipeline to monitor the status of individual subtasks constituting eThread and thus can conduct the workflow efficiently to maximize the use of available resources on the fly while supporting various optimization scenarios. By exploiting this feature, data parallelization and task-level parallelization are easily implemented. For example, a simple task-level parallelism could be designed as shown in How to run eThread on EC2 is critically important since the cost and the time-to-solution will increase considerably without optimization, and to some extent, making the pipeline unpractically expensive or time consuming for genome-scale analysis. Note that, due to the charging scheme from Amazon, two conditions for the optimization are not equivalent. For example, in Contrast to the execution mode of eThread using a single computer system or a cluster system, the eThread pipeline implemented with SAGA-Pilot cannot avoid an overhead due to its underlying Master-Worker model. The overhead, first of all, arises from the data transfer between a local machine that runs the main script and remote compute resources in EC2  implementations and data parallelization with BLAST to distribute chain and domain library searches are possible, we can further divide into many subtasks from each elementary task. This gives also a benefit for memory footprint and thus is beneficial to use less power but more number of instances. For example, SAMT2K requires 6 GB RAM due to its BLAST task and could be implemented with such parallel BLAST. Our approach is mostly scale-out at this point but needs to consider scale-up approaches with advanced accelerator techniques such as GPGPU, Intel Phi, and other emerging technologies including IBM Coherence Attach Processor Interface (CAPI).eThread is expected to play an important role for genome-scale structural bioinformatics analysis. In spite of its better performance and structural information, in particular, for annotation purposes, required computational demands hinder its usage. To address such a challenge, we developed the SAGA-Pilot-based eThread pipeline and conducted benchmarks aiming at the efficient use of Amazon EC2 infrastructure. With demonstrative examples, we show the support of various data and task-level parallelization scenarios on heterogeneous resources available from Amazon EC2, implying that further optimization ideas including dynamic scheduling could lead to eThread as a practically powerful tool. Other IaaS cloud environments, employing open standards such as OpenStack, are immediately ready to run the eThread pipeline.http://dare.cct.lsu.edu/), which provides a service of the eThread pipeline [Among many potential uses, the eThread pipeline has been developed as a genome-scale annotation tool as a part of an integrative Next-Generation Sequencing (NGS) data analytics. Our continuing effort to build NGS data analytics science gateway, which focuses on utilization of scalable distributed computing and storage resources, is underway (see pipeline . Based o


Molecular research in cancer is one of the largest areas of bioinformatic investigation, but it remains a challenge to understand biomolecular mechanisms in cancer-related pathways from high-throughput genomic data. This includes the Nuclear-factor-kappa-B (NFκB) pathway, which is central to the inflammatory response and cell proliferation in prostate cancer development and progression. Despite close scrutiny and a deep understanding of many of its members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. Here, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for ATF3, CXCL2, DUSP5, JUNB, NEDD9, SELE, TRIB1, and ZFP36 in this pathway, in addition to new mechanistic interactions between these genes and 10 known NFκB pathway members. A newly predicted interaction between NEDD9 and ZFP36 in particular was validated by co-immunoprecipitation, as was NEDD9's potential biological role in prostate cancer cell growth regulation. We combined 651 gene expression datasets with 1.4M gene product interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction among pathway members were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in a total of 112 interactions in the fully reconstructed NFκB pathway: 13 (11%) previously known, 29 (26%) supported by existing literature, and 70 (63%) novel. This method is generalizable to other tissue types, cancers, and organisms, and this new information about the NFκB pathway will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies. Nuclear-factor-kappa-B (NFκB) pathway. Despite close scrutiny and a deep understanding of many of the NFκB pathway members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. In this study, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for 8 genes in this pathway and new mechanistic interactions between these genes and 10 known pathway members. We combined 651 gene expression datasets with 1.4M interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in 112 interactions in the fully reconstructed NFκB pathway. This method is generalizable, and this new information about the NFκB pathway will allow us to further understand prostate cancer.In molecular research in cancer it remains challenging to uncover biomolecular mechanisms in cancer-related pathways from high-throughput genomic data, including the  Proteins in the nuclear-factor-kappa-B (NFκB) complex belong to a family of transcription factors  that regulate expression of genes involved in immune and inflammatory responses, cell growth, differentiation, and apoptosis. While these proteins are highly pleiotropic, their activation is context-specific , the NFκProstate cancer cells in particular have been reported to have constitutive NFκB activity due to increased activity of the IκB kinase complex, which can lead to cell growth and proliferation, while apoptosis is inhibited in prostate cancer cells ,7–12. GeIt is now possible to predict detailed, mechanistic interactions and pathway components using large-scale computational data integration ,21. ThisIn this study we address this challenge and provide the first steps toward computational recovery of mechanistic pathway components specific to the NFκB pathway as perturbed in prostate cancer . This waIn this study we suggest a new mechanistic NFκB pathway in human prostate cancer . This paThe predicted NFκB pathway specific to prostate cancer consisted of 50 genes connected by 112 biomolecular mechanisms : of thesAs a first step, we tested whether the genes newly predicted to this pathway were enriched for pathways, diseases, or biological processes from the Gene Ontology, KEGG, or the Pathway Interaction Database associated with tumorigenesis or prostate cancer  S13A Ta. InteresThe NFκB complex consists of 5 proteins  and, upon activation, provides a powerful defense mechanism against infection and stress; regulation of the complex in managed in part by families of NFκB inhibitor genes (IκB) and kinases (IKK) ,7. Here,In particular, our results predicted BCL2 and several inflammatory chemokines to be novel downstream targets of NFκB, including the anti-apoptotic protein BCL2A1, the chemokine (C-X-C motif) ligand 1 (CXCL1), and (C-C motif) ligand 8 (CCL8). This conclusion is based on a predicted direct binding and downstream regulation of BCL2A1 by the NFκB complex , a predicted downstream regulation of CCL8 by NFκB2, and a direct binding of CXCL1 with NFκB2 and RELB . This isIn addition to downstream targets of NFκB, we also recovered important upstream regulators for cancer development and progression, including STAT3, MAP3K8, and TNF. Our predictions suggested that STAT3  and the bone morphogenetic protein BMP2 concordantly influence NFκB in prostate cancer by predicted direct interaction, which complements previous studies showing that BMP2 induces apoptosis with modulation of STAT3 . AdditioThe oncogene MAP3K8 was correctly predicted as an upstream activator of NFκB and activator of both the MAP kinase and JNK kinase pathways, which leads to an activation of downstream genes such as c-Jun, and JUNB, an AP1 transcription factor and oncogene ,48. ThisAnother gene that was predicted to directly interact with the JNK kinase pathway, in particular JUNB , was theWe predicted several genes to act in prostate cancer as novel upstream regulators of NFκB or novel downstream regulatory targets of NFκB. These included 8 genes differentially expressed in lethal prostate cancer and highly functionally related to NFκB  along wiIn particular, we predicted the nuclear receptor NR4A2 and the activating transcription factor 3 (ATF3) as novel upstream regulators of NFκB in prostate cancer. This was based on the observation that ATF3 was predicted to be highly functionally related to NFκB during regulation of cell cycle and cytokine metabolic process  and to iWe correctly predicted and confirmed the NFκB inhibitors ε (NFκBIE)  and α N. AdditioIn addition to these novel upstream regulators of NFκB in prostate cancer, we also predicted new downstream targets, including several cytokines and a selectin. Our prediction of the chemokine (C-X-C motif) ligand 1 and 2  as direct and indirect downstream targets of NFκB in prostate cancer can be supported by previous finding in different contexts ,51,65,66We inferred NFκB pathway components in prostate cancer using information from 860 total datasets. 651 of these were gene expression studies, of which 18 were included specifically due to profiling prostate cancer tissues. We additionally incorporated 225 interaction networks  together comprising 1.4M interactions. These data were unified into a predicted set of pathway-specific interactions using a Bayesian framework to model the probability of each dataset providing accurate results relevant to disease pathways in prostate cancer ,71. ThisTo identify interactors within the NFκB pathway in each context, we extracted the subnetworks most confidently associated with the NFκB1 gene, i.e. its nearest neighbors, which resulted in 66 genes in total . Among tNext, we analyzed which of these 66 genes showed a significant change in gene expression (at a significance level of 5% after FDR correction) between lethal and indolent prostate cancer see  25,26],,26,25,26The eight genes found to be significantly down-regulated in prostate cancer  were furTo quantitatively assess the expected performance of our predicted context-specific networks , we perfAs outlined above, we produced one context-specific network for each interaction mechanism based on our interaction ontology  and usinTo assess the performance of these predicted mechanism-specific networks, we performed a 10-fold gene-holdout-based cross-validation, yielding an average AUC of 0.75±0.02 (95% CI) across all mechanisms . CompariIn line with a previous study , these rAs outlined above and detailed in Methods, we predicted individual mechanism-specific networks based on a multi-labeled hierarchical classification formulation ,75. ThisTo computationally estimate the true and false positive prediction rates of our approach, we compared our predictions with (1) a prostate cancer-specific co-expression analysis, (2) known mechanisms from the NFκB signaling pathway in BioCarta , and (3) known mechanisms from literature. This revealed a high area under the curve (AUC) value of 0.83 after comparing our predictions with co-expression, along with true positives for 78.5% of all positive predictions with a precision rate of 66.1% for the interactions from BioCarta and PubMed, as detailed below.First, we compared our predicted NFκB pathway with gene co-expression relationships retrieved from the TCGA dataset see . This enSecond, we compared our predictions with reported interactions from the NFκB signaling pathway in BioCarta and literature see . This coThird, we compared 50 known NFκB interaction mechanisms from the literature (NCBI) with our predictions see  Tables, To validate our computational reconstruction of novel interactions, we chose to assay the potential interaction between NEDD9 and ZFP36 by co-immunoprecipitation  experimentally validate their molecular mechanisms of action and 2) evaluate gene sets derived from our extended pathway as potential clinical biomarkers for prostate cancer risk . The bulk of newly predicted pathway components represent physical protein-protein interactions: stable co-complexing or transient interactions such as post-translational modifications (e.g. phosphorylation). These should be assayed by extending our preliminary co-immunoprecipitations with additional targets and antibodies, using complex-targeted techniques such as TAP-tagging, and (when possible) specifically assessing protein state by phosphoantibody or mass spectrometry targeting. Transcriptional regulatory predictions, especially those downstream of NFκB itself , can be more easily assessed by qPCR readout in the presence of knockdown or other perturbations. Each of these targeted experimental readouts could then be re-incorporated into a refined prediction model to further extend or increase confidence in NFκB pathway components.More importantly, molecular epidemiological data are needed to link these genes’ activities  to prostate cancer severity and outcome. Many patients are not destined to progress to higher grade and potentially lethal disease, and they can thus avoid surgery or radiotherapy if their low risk is detected early by molecular or other biomarkers. Purely expression-based biomarkers of low prostate cancer progression risk have yet to be identified; a more detailed mechanistic perspective (as provided by our predictions) may lead to targeted transcript assays or to sets of informative gene products (e.g. phosphoproteins). Additionally, more nuanced molecular predictors might identify patients with high risk of micrometastatic disease at time of surgery or radiotherapy, who would then be in need of systemic adjuvant therapy to prevent relapse and death. Data assessing the transcriptional and post-translational states of genes in the extended NFκB pathway along with clinical outcome should therefore be collected. The tightly linked pathway components predicted here thus represent one new step along the route to more effective molecular therapies and diagnostics in prostate cancer.http://huttenhower.sph.harvard.edu/cap and http://dx.doi.org/10.7910/DVN/WPRDBZ, and for more details on the methods please refer to the To recover the mechanistic pathway components specific to the NFκB pathway as perturbed in prostate cancer , we firsWe integrated high-throughput and heterogeneous functional genomic data (see below) using a naïve Bayesian approach with regularization ,71. BrieTo predict context-specific functional networks for a set of 9 relevant biological contexts , we firsTo assign a biomolecular mechanism to each functionally related gene pair in the final network, we constructed an interaction ontology consisting of seven interaction types and applied an integrated method for concurrently predicting multiple protein interaction types  Fig 4A)Fig 4A). Next, we used an integrated method for concurrently predicting multiple protein interaction types  to assigk = 10 using the HEFalMp ratio query algorithm (see above). Next, we integrated these subgraphs into one pathway in which genes were connected by high-confidence biomolecular mechanisms , their inhibitors , and eight genes significantly down-regulated in lethal prostate cancer   as identrelation ,71, and relation .We computed a normalized correlation measure for each gene pair in each dataset to assess a similarity score as co-expression for all gene pairs ,71,119. To generate a gold standard specific to the NFκB pathway, we manually chose 30 pathways from the PathwayCommons database  S2 Tabl that 1) Out of 442 biological processes from Gene Ontology, our medical team manually chose nine biological contexts that play an important role in human prostate cancer development and progression: cell death, cell differentiation, cell cycle, cell proliferation, cell migration, cell motion, vascular development, cytokine metabolic process, or NFκB regulation. These contexts consist of gene lists with a number of genes ranging from 37 to >1,000 . For eacAs our interaction ontology consists of seven hierarchically organized biological mechanisms , we acco’ Health Study (PHS) Prostatectomy Confirmation Cohort from the United States [p-values using the R/limma package, revealing 186 out of 6,096 genes as differentially expressed at a significance level of 5% after FDR correction (We used a gene expression dataset which is based on a set of four complementary DNA (cDNA)–mediated annealing, selection, ligation, and extension (DASL) assay panels (DAPs) for the discovery of molecular signatures relevant to prostate cancer for 116 male patients with prostate cancer from an inhouse Physiciansd States ,26. To arrection .th, 2013 Broad Institute Firehose run (http://gdac.broadinstitute.org). We applied RSEM abundance quantification at the gene level, where values were normalized to set the upper quartile count at 1,000 reads. A complete description of the TCGA data processing pipeline is available at the TCGA data portal (https://tcga-data.nci.nih.gov) in the MAGE-TAB annotation files. Next, this dataset was integrated into a single prostate cancer-specific co-expression network using unsupervised data integration averaging across normalized co-expression values (z-scores) [We obtained RNA-Seq Level 3 data from the October 12-scores) .We computationally estimated the true positive rate and precision of our predictions by comparing with known interaction mechanisms from 1) the NFκB signaling pathway in BioCarta  and literature, 2) NCBI, and 3) prostate cancer-specific co-expression analysis based on the TCGA dataset (see above).First, we compared predictions from our Bayesian network model based on the interaction ontology  with 31 Second, we compared 50 known mechanisms from the literature with our predictions. Here, we chose the top 50 mechanisms (25 for each NFκB1 and TNF) which were absent from the gold standard and for which literature evidence was available from NCBI gene catalogue  Tables. p-values with our confidence scores for all 112 predicted interactions in the pathway. As a negative control, we randomly selected the same number of predicted interactions with low predicted confidence scores from the genome.Third, we compared our high-confidence predicted interactions from the novel NFκB pathway with co-expression data derived from TCGA. Using these data, we computed co-expression values for all gene pairs and compared the derived We performed two screens for NEDD9 activity and ZFP36 interaction: co-immunoprecipitation and siRNA knockdown in a prostate cancer cell line. LAPC4 cells were received from Dr. Robert Reiter, University of California, Los Angeles. These were maintained in RPMI 1640 at 37°C, 5% CO2, and 100% relative humidity and supplemented with 10% FBS and 100 IU of penicillin and streptomycin (100 μg/ml).For NEDD9 / ZFP36 interaction testing, LAPC4 cells were lysed by RIPA buffer and protein concentration was measured by protein BCA assay (Bio-Rad). Cell lysis of 500 μg was applied for each immuno-precipitation for NEDD9 and ZFP36. Rabbit IgG was included as a control and the results were analyzed by western blot with NEDD9 (Fisher Scientific).For knockdown, we read out cell proliferation as a phenotype using LAPC4 cells cultured until ~80% confluence and then transfected with siRNAs (Origene) using lipofectamine 2000. NEDD9 siRNA probes 1 through 3 were purchased as catalog #SR303132 with sequences CCCAAGAACAAGAGGUAUAUCAGGT, GGCCUUAUAUGACAAUGUCCCAGAG, and CAACAGAAGCUCUAUCAAGUGCCAA, respectively. Knockdown efficiency was detected by western blot at 3 days after transfection. For cell proliferation, cells were split into 96-well plate with a confluence of ~40% after siRNA transfection for 24 hours. The cell proliferation assay was carried out at different days after splitting using the WST-1 assay (Roche) with the detection of the absorption at a wavelength of 450 nm (following manufacture instructions). Each experiment was performed in triplicate.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 FigFor each gene pair with a known interaction mechanism we extracted their corresponding predicted mechanism(s) from our predicted interaction networks using a threshold of 0.96 (representing the top 5% highest-confidence predictedgene interactions).(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 FigTop: Comparison of all high-confident predictions from the novel NFκB pathway with co-expression values as retrieved from the TCGA dataset, revealing 70% of predicted high-confidence interactions in this pathway to be co-expressed in the TCGA dataset as well see . Bottom:(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableAll contain the NFκB1 gene and at most 200 genes.(XLSX)Click here for additional data file.S3 TableTo make the contexts prostate cancer-specific, we overlapped the corresponding gene sets with differentially expressed genes from the GXA database see .(XLSX)Click here for additional data file.S4 TableDue to the interaction hierarchy, interaction parents  inherited known positive interactions from their interaction children , with equal amounts of random (negative or not related) gene pairs representing negative interactions.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 TableFor each gene pair with a known mechanism from BioCarta we extracted their corresponding predicted mechanism(s) from our corresponding predicted interaction networks . By comparing known mechanisms with their corresponding predictions, we estimated the number of true positives (TP), which are predictions that match the known mechanisms, and false positives (FP), which are predictions that are not among the known mechanisms.(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableWe again extracted all high-confidencemechanisms for all gene pairs at a threshold level of 0.96 representing the top 5% highest-confidence gene interactions.(XLSX)Click here for additional data file.S11 TableWe again extracted all high-confidencemechanisms for all gene pairs at a threshold level of 0.96 representing the top 5% highest-confidence gene interactions.(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S14 TableThe AUC values are the median values across all 10 cross validations per mechanism.(XLSX)Click here for additional data file.S15 Table(A) Comparison of all high-confidence predictions from the novel NFκB pathway  with co-(XLSX)Click here for additional data file.S16 Table(XLSX)Click here for additional data file.S17 TableFor some predicted interactions, there is no such interaction known, but there is evidence that both interactors are upregulated together (C). Here, out of 112 predicted mechanisms, we observed 13 (11%) to be present in the gold standard, while 29 (26%) were confirmed in the literature, and 18 (16%) were reported to be coregulated.(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.