One key challenge is to detect functional driver pathways and to filter out nonfunctional passenger genes in cancer genomics. Vandin de novo from somatic mutation data utilizing two combinatorial properties, high coverage and high exclusivity, without any prior information. We firstly construct gene networks based on the approximate exclusivity between each pair of genes using somatic mutation data from many cancer patients. Secondly, we present a new greedy strategy to add or remove genes for obtaining overlapping gene sets with driver mutations according to the properties of high exclusivity and high coverage.To find a better solution and solve the problem more efficiently, we present a network-based method (NBM) to detect overlapping driver pathways automatically. This algorithm can directly find driver pathways or gene sets To assess the efficiency of the proposed NBM, we apply the method on simulated data and compare results obtained from the NBM, RME, Dendrix and Multi-Dendrix. NBM obtains optimal results in less than nine seconds on a conventional computer and the time complexity is much less than the three other methods. To further verify the performance of NBM, we apply the method to analyze somatic mutation data from five real biological data sets such as the mutation profiles of 90 glioblastoma tumor samples and 163 lung carcinoma samples. NBM detects groups of genes which overlap with known pathways, including P53, RB and RTK/RAS/PI(3)K signaling pathways. New gene sets with p-value less than 1e-3 are found from the somatic mutation data.NBM can detect more biologically relevant gene sets. Results show that NBM outperforms other algorithms for detecting driver pathways or gene sets. Further research will be conducted with the use of novel machine learning techniques. Cancer has become one of the most serious threats to human health. Cancer is driven in part by somatic mutations, including single nucleotide substitutions, small indels, large copy number aberrations, and structural aberrations that accumulate during the lifetime of an individual . At the The final decision of whether a gene mutation is a driver or a passenger is to be made after testing its biological function. However, it is expensive at present to detect somatic mutations and to validate their functions experimentally . A commode novo from somatic mutation data utilizing two combinatorial properties, namely - coverage and exclusivity [It is an urgent priority to predict driver mutated genes and pathways through computational approaches. A large number of computational methods have been developed to address these challenges. There are mainly two computational approaches: one approach is to infer driver mutated genes and pathways by integrating somatic mutation data and additional biological knowledge such as protein-protein interaction networks, gene expression data or other sources of information ,4-6, andlusivity ,7,8, rathttps://tcga-data.nci.nih.gov/tcga/), Kyoto Encyclopedia of Genes and Genomes (KEGG) [In recent years, the declining costs of genome sequencing allow measurement of the somatic mutations in many cancer genomes. Many research results, including those from The Cancer Genome Atlas (TCGA) website (s (KEGG)  and othes (KEGG) . One of s (KEGG)  or knowns (KEGG) . More reDe novo Driver Exclusivity (Dendrix) [The known gene and protein interaction networks in humans remain incomplete, and the information about the interactions in these networks is unreliable and imprecise ,12. The Dendrix)  algorithDendrix)  improvesDendrix) .de novo from somatic mutation data without any prior biological knowledge. In the first step, we filter the mutation matrix and reserve the genes which meet a certain frequency of recurrence, as genes altered in only one or few cancer patients may not be driver genes but passenger genes. So we remove the genes whose mutation numbers are lower than 5 percent of all cancer patients. In the second step, we construct a gene network by calculating the exclusive score between each pair of genes. If the exclusive score between a pair of genes is greater than or equal to a threshold λ, the edge between the pair will be created, and its weight is the exclusive score. In the third step, we present a novel greedy growth process based on the concept of high coverage and high exclusivity to find gene sets in the gene network constructed in the previous step. These gene sets are likely to correspond to driver pathways.To reduce the complexity of the solution and solve the NP-hard problem of the Maximum Weight Sub-matrix in an efficient approach, we present a new NBM algorithm to detect functional driver pathways et al. [high coverage" which means many patients have at least one mutation in the pathway [high exclusivity" which means most patients have no more than one mutation in the pathway [A with m rows (samples) and n columns (genes), the exclusive degree function ED(M) and coverage degree function CD(M) are defined as follows. For a gene g, the coverage Γ(g) = {i: ig A- 1} represents the set of patients in which gene g is mutated  = ∪g∈M Γ(g). M is mutually exclusive if Γ(jg) ∩ Γ(kg) = ∅, for all jg, kg, ∈ M, j g≠ kg, 1 ≤ j, k ≤ n A gene set in A named as a driver pathway is a column sub-matrix of A with high coverage and high exclusivity.Two important characteristics on the expected modes of somatic mutations have been used to understand the somatic mutation process of cancer in the last couple of years. Vandin et al.  introduc pathway ; the sec pathway . Given aM of size m × k, the exclusive degree function ED(M) is defined as:For a sub-matrix jg, kg, the exclusive degree of them is defined as:Obviously, for a pair of genes ED(M) = 1 when M is mutually exclusive.According to the above analysis, M of size m × k, the coverage degree function CD(M) is defined as:For a sub-matrix jg, kg, the coverage degree of them is defined as:Obviously, for a pair of genes CD(M) = 1 when M is the complete coverage.Note that M are ED(M) = 1 and CD(M) = 1, but it is practically impossible. Obviously, we want to get the optimal results for the exclusive degree and the coverage degree at the same time, but increasing coverage may be obtained at the expense of decreasing exclusivity. So we define a function named exclusivity-coverage-degree as follows:The optimal results for a sub-matrix jg, kg, the exclusivity-coverage-degree is defined as:Obviously, for a pair of genes ECD(M) = 1 when M is the mutually exclusive and complete coverage. We need to find a gene set M whose exclusive degree and coverage degree are large simultaneously, so we define a function ECD(M) which allows for a tradeoff between exclusivity and coverage.Note that et al. introduced the Maximum Weight Sub-matrix Problem, which is defined for an integer k > 0 as the problem of looking for a m × k sub-matrix M of A that maximizes a weight W(M) [approximate exclusivity between each pair of genes to simplify the relationships among genes and to reduce the calculation complexity greatly. First, we calculate the exclusive degree of each pair of genes in a somatic mutation matrix using formula (2). Second, we construct a gene network in which each node represents a gene and the weight of an edge represents the exclusive degree between the pair of genes that is greater than or equal to a threshold λ. If the exclusive degrees between one gene and the rest of all other genes are less than the threshold λ, the gene will not appear in the network. The process is shown in Figure Vandin ght W(M) ,7,8, andhigh coverage and high exclusivity from selected two seeds. Initially, it selects two connected nodes with the highest exclusivity-coverage-degree as the first two seeds, and then grows the gene set from them using a greedy procedure. Whenever the growth process finishes, the algorithm selects the next two connected seeds by considering all the nodes that have not been included in any of gene sets found so far and taking the two connected seeds with the highest exclusivity-coverage-degree again. The whole procedure terminates when there are no two connected nodes remaining to consider.Our clustering method consists of two steps. In the first step, the algorithm grows gene sets with v0, v1 is as follows [A step-by-step description of the greedy growth process beginning from two connected nodes  follows .V0 = {v0, v1}. Set the step number t = 0.Step 1: Let exclusive degree and exclusivity-coverage-degree of t Vand let Vt+1 = tV.Step 2: Calculate the v incident on at least one boundary edge, calculate the exclusive degree and exclusivity-coverage-degree of V' = t V∪ {v}. If ECD(V') >ECD(Vt+1) and ED(V') >λ, let Vt+1 = V'.Step 3: For every external node v which belongs to tV, calculate the exclusive degree and exclusivity-coverage-degree of V' = Vt\{v}. If ECD(V") >ECD(Vt+1) and ED(V") >λ, let Vt+1 = V".Step 4: For every internal node Vt ≠ Vt+1, increase t and return to step 2. Otherwise, declare Vt a locally optimal driver pathway.Step 5: If high exclusivity and high coverage cluster grown from different seed nodes absorbs them.The growth process permits the removal of any node from the gene set being grown, including the original seed nodes. If the original one or two seed nodes are not included in the final driver pathway, they are considered outliers and they will not be included in any of the driver pathways, except in the case when another coverage degree is below a given threshold δ. For the gene sets whose coverage degree is too low, they are usually not regarded as driver pathways.In the second step, we discard driver pathway candidates that contain less than three genes or those whose threshold λ is used to decide whether there is an edge between each pair of genes according to its exclusive degree. Threshold δ is applied to determine whether a driver pathway candidate is chosen as a driver pathway according to its coverage degree. Based on the analysis of the algorithm and a large number of experiments, the following threshold values have been identified as leading to good results λ = 0.95 and δ = 0.3.In the algorithm, approximate exclusivity between each pair of genes from somatic mutation data. Finally, we obtain driver pathways based on the exclusive degree and exclusivity-coverage-degree using a new greedy strategy.To assess the efficiency of NBM, we apply the method on simulated data and compare the results with Multi-Dendrix , with thm = 300 patients and n = 500 genes as follows. Five pathways P =  with each Pi containing five genes are implanted in the mutation matrix. We select the coverage Γ(iP) uniformly whose coverage degrees CD(iP) are 0.95, 0.85, 0.75, 0.65, 0.55, respectively. For each pathway iP, |Γ(iP)| patients are selected at random and a driver mutation is added to exactly one gene from the pathway iP. Therefore, in each pathway iP, the driver mutations are mutually exclusive. Then passenger mutations are added at random with probability of q.We generate mutation data for q.We compare NBM with Multi-Dendrix , iteratiTo assess the performance of our NBM method on real biological data, we collect five somatic mutation data sets which are obtained from  directlyet al. [k = 3, and NBM can obtain also the gene set . The exclusive degree and coverage degree of the gene set  are 88.7% and 65.4% respectively, and the exclusive degree and coverage degree of the gene set  are 95.2% and 60.1% respectively. Obviously, the exclusive degree of our results is higher than that of the results obtained from the above three methods. It is necessary to acknowledge that the coverage degree declines at the expense of increasing exclusive degree. Besides, in our results, EGFR and NF1 are members of the RTK/RAS/PI(3)K signaling pathway involved in cellular proliferation, and EGFR and NF1 are associated with classical and mesenchymal subtypes, respectively. EGFR interacts with KRAS, and NF1 inhibits KRAS in the RTK signaling pathway [We first apply NBM to the lung adenocarcinoma data set used by Vandin et al.  to assesIn the following sections, we further apply our method NBM on the five datasets that are presented in Table et al analyzed whole-exome sequencing data from 74 cancer-normal pairs and revealed many genes that have not been related to malignancy in previous studies [HNSCC is a common and frequent lethal malignant tumor which is the sixth leading cancer according to incidence worldwide . To unco studies . The mutk is 3. The gene set is altered in 43.3% with p-value = 4.95e-03. In the third driver pathway, CSMD3 mutation has been reported as an important factor in HNSCC [In the first driver pathway, TP53 is the core member of the p53 signaling pathway. DCHS1 can lead to a recessive syndrome in humans which includes periventricular neuronal heterotopias. These will affect the transcriptional effectors of the hippo signaling pathway . PIK3CA in HNSCC . FAT1 isin HNSCC . The genGlioblastoma multiforme (GBM) is the most common and most aggressive type of primary brain tumor. In the GBM analysis, both copy-number aberrations and single-nucleotide  mutations are contained to form mutation matrix . Eight gk is 3. The gene set is altered in 83.3% with p-value = 6.46e-03. In the second driver pathway, CDKN2A and TP53 are the members of the p53 signaling pathway. Somatic genetic CDKN2A and TP53 alterations are common in many human cancers and their precursors [In the first driver pathway, CDKN2B and RB1 are the core members of the cell cycle and cell cycle mitotic. CYP27B1 is the member of the glioblastoma copy number up and ERBB2 is the member of the cancer copy number up. The three genes  are sampled as the most frequent result in [ecursors . The genecursors . The genecursors . The genThe glioblastoma dataset is obtained from TCGA 2008). It contains gene expression profiles nucleotide sequence aberrations and DNA copy number alteration in 206 glioblastomas samples . A mutat008. It cIn the first driver pathway, CDKN2A and TP53 are the core members of the p53 signaling pathway; CDKN2A, CDK4 and RB1 are the core members of the RB signaling pathway. CDKN2A activates TP53 in the p53 signaling pathway. CDKN2A inhibits CDK4 and CDK4 inhibits RB1 in the RB signaling pathway. The gene set is altered in 83.3% with p-value = 2.64e-8. In the second driver pathway, PTEN, PIK3CA and EGFR are the core members of the RTK/RAS/PI(3)K signaling pathway which is prominently altered in glioblastoma. PTEN inhibits PIP3; EGFR activates PIK3CA; PIK3CA activates PIP3 in the RTK/RAS/PI(3)K signaling pathway. The gene set is altered in 57.8% with p-value = 1.17e-05. In the third driver pathway, CDK4, CDKN2B and RB1 are the core members of the RB signaling pathway. CDK4 inhibits RB1, and CDKN2B inhibits CDK4 in the RB signaling pathway. The gene set is altered in 73.3% with p-value = 7.62e-05. In the fourth driver pathway, PIK3CA, PTEN and PIK3R1 are the core members of the RTK/RAS/PI(3)K signaling pathway which is prominently altered in glioblastoma. PTEN interacts with PIK3R1; PIK3R1 interacts with PIK3CA; PTEN inhibits PIK3CA in the RTK/RAS/PI(3)K signaling pathway. The gene set is altered in 53.3% with p-value = 1.17e-05. In the fifth driver pathway, MDM4, TP53 and MDM2 are the core members of the p53 signaling pathway. QKI is a member of the signal transduction and activation of RNA family of RNA-binding proteins, as a novel glioblastoma multiforme tumor suppressor . The TP5et al. analyzed a collection of 1013 somatic mutations from 188 lung adenocarcinoma patients [Vandin patients . There aIn the first driver pathway, TP53 and ATM are the core members of the p53 signaling pathway. ATM activates CHK1/2, and CHK1/2 activates TP53 in the p53 signaling pathway. The gene set is altered in 46.6% with p-value = 1.74e-3. In the second driver pathway, TP53, ATM and RB1 are the core members of the p53 signaling pathway. ATM has been reported as a mutated tumor suppressor gene in lung adenocarcinoma and it encodes a cell-cycle checkpoint kinase as a regulator of the p53 signal pathway . ATM actOvarian cancer often goes undetected until it has spread within the abdomen and pelvis. At this late stage, it is very difficult for ovarian cancer to treat and is frequently fatal . The datIn the first driver pathway, TP53 and CCND2 are the core members of the p53 signaling pathway. TP53 activates CCND2 in the p53 signaling pathway. The gene set is altered in 82.4% with p-value = 1.74e-03. In the second driver pathway, TLR3 is the member of the TLR signaling pathway which has been implicated as having both tumor-promoting and tumor-suppressive on ovarian cancer . TP53 isde novo from the somatic mutation data of cancer without any prior biological knowledge of gene expression data, pathways and interactions between genes. In this algorithm, gene network is firstly constructed according to high exclusivity to solve the problem of high complexity encountered in the previous methods. Then a new greedy algorithm is introduced to cluster gene sets according to the properties of high exclusivity and high coverage. The results indicate that integrative analyses of somatic mutation data have the potential to detect gene sets pertinent to cancer phenotypes. Moreover, the algorithm is also capable of finding the functional relevance of uncharacterized or unexpected genes.Finding mutated driver pathways in cancer is an essential problem in computational biology. In this paper, we introduce a novel algorithm called NBM for automatically discovering mutated driver pathways in cancer using somatic mutation data from many cancer patients. Our algorithm can find gene sets few mutated together in the same patient (high exclusivity) and mutated in many samples (high coverage). Notably, we find these mutation pathways Comparing with the previous methods of finding driver mutation pathways, our algorithm is superior in the following two aspects. Firstly, the complexity of the solution is reduced by constructing gene networks from somatic mutation data. Secondly, there is no need to assign the number of genes in a driver pathway with our algorithm. It is necessary to point out that our algorithm does not use gene interaction data, pathways and other biological information. The algorithm could provide a supplement to the analyses of cancer data and does not replace human analyses. We also anticipate that this method will be increasingly helpful in producing hypotheses that will drive some specific experiments and increase understanding for cancer progression. Further development is anticipated with the use of machine learning techniques . We alsoDe novo Driver Exclusivity; RME: Recurrent and Mutually Exclusive; TCGA: The Cancer Genome Atlas; KEGG: Kyoto Encyclopedia of Genes and Genomes; HNSCC: Head and Neck Squamous Cell Carcinoma Data; GBM: Glioblastoma Multiforme Data; LC: Lung Carcinoma Data; OC: Ovarian Carcinoma Data.NBM: Network-Based Method; Dendrix: The authors declare that they have no competing interests.Conceive and design the experiments: HW LG. Perform the experiments: HW XY. Analyze the data: HW FL. Contribute reagents/materials/analysis tools: HW FS. Write the paper: HW. Consult on the final version of the paper and edit the paper: HW LG NK. The authors read and approve the final version of the manuscript.


Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-κB and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer's disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF- In pathomorphism, the expression is senile plaques (SP) and neurofibrillary tangles (NFT).Alzheimer's disease (AD) is a progressive and fatal neurodegenerative disorder manifested by cognitive and memory deterioration. The characteristic pathology changes in AD are fibrin deposition in cerebral cortex; it is the deposition of beta-amyloid  networks are reconstructed from protein domain characteristics, gene expression data, and structure-based information with other evidence, for example, gene homology, function annotations, and sequence motifs . PPI datκB (nuclear factor-kappa B) as one of the most important inflammatory factors is selected as the starting gene of the signaling pathway. As we know that NF-κB plays a key role in regulating the immune response to infection, therefore incorrect regulation of NF-κB has been linked to cancer, inflammatory and autoimmune diseases, septic shock, viral infection, and improper immune development. NF-κB has also been implicated in processes of synaptic plasticity and memory [β is arranged as the ending protein of the predicting pathway.Based on that, ILP model is presented to reconstruct the inflammatory signaling pathway by integrating PPI data with the AD gene expression data. In the ILP model, the starting and ending proteins of the predicting pathway need to be arranged in advance. Nuclear transcription factor NF-d memory . On the The experiment results show that 6 AD virulence genes are identified being included in the predicted inflammatory signaling pathway, and a large amount of inflammation related genes and pathways has been found by molecular biological analysis and they show the great understanding of the pathogenesis of AD.κB and APP. The usual linear regression model can be expressed asμ = T is response vector, n is the sample number, xj = T, j = 1,2, …, p, is the predictor, represented by the jth gene's expression data in all samples, and βj is the jth gene's weight vector. Assume that the predictors are standardized and the response is centered, we getLinear regression model is widely used in estimation and variable selection. In our study, the model is applied to selected subset of significant genes which are important for AD and are going to be the transduction factors of reconstructing pathway. In the next prediction step, gene expression data and PPI data will be integrated by ILP model. After all of the above, a pathway could be identified between NF-Gene expression data has the characteristic of less sample and great noise. As a simple model, linear regression model has significant performance in handling less sample and great noise data. The significant genes will get a larger coefficient while the nonsignificant genes will get a smaller coefficient.β needs to be estimated. Many methods have been proposed which focused on addressing high-dimensionality genomic data such as LASSO, LA-SEN, and LARS. Here, a modified network-constrain regularization analysis by C. Li and H. Li [L asw represents the weight of edge between linked genes u and v. dv = ∑u~vw represents all the adjacent genes of v on the network. Then the definition of the network-constrained regularization criterion isX = (x1|K|xp); |β1| = ∑j=1p|βj|; λ1, λ2 are nonnegative turning parameters. And then we estimate β by minimizing , where V is vertices in the graph, representing protein; E is edge between proteins; and W represents the weight of edges. W can be calculated by gene expression data. The ILP model can be described as follows:wij is the weight between proteins i and j in weighted undirected graph G; yij is a binary variable to denote whether the edge E is a part of the STN. xi is also a binary variable to denote whether protein i is a component of the STN. λ is a positive penalty parameter. |V| includes all proteins in the PPI network. yij ≤ xi and yij ≤ xj mean that only if proteins i and j are both components of STN, the edge E should be considered. ∑j=1V||yij ≥ 1 represents at least one protein contact with starting protein or ending protein. ∑j=1V||yij ≥ 2xi makes sure that if xi is selected as a component of STN, there are at least two proteins link to the vertex.The ILP model formulates signaling network detection as an optimization problem and treats a signaling network as a whole entity as described in its original publication . PPI netλ can be found in its original publication. Then we detected protein pathway by the ILP model.The starting protein and ending protein have confirmed above that the genes selected by linear model were treated as transduction factors. The method of chosen parameter http://thebiogrid.org/) with 12466 proteins and 40323 interactions in total.To evaluate ILP model, AD dataset, series GSE1297, was used which were human hippocampal gene expression downloaded from GEO DataSets from the National Center for Biotechnology Information (NCBI) offered by Blalock et al. . The hipP value was less than 0.05. After processing, 7030 genes for each sample were left. Then taking linear regression model with modified network-constrained regularization and AD biological information, the coefficients β =   and  p = 7030 were obtained. Among them, 7017 values of β were zeroes and the other 13 βswere nonzero values. So these 13 genes with nonzero values of β  were considered as significant genes to AD phenotype and they are denoted in green circles with “(1)” and gene names in The file format of microarray data downloaded from NCBI is CEL. The probe data needs data processing like background correct, normalization, probe correct, and so on. Then ANOVAs were used on preliminary select genes and removed all genes whose i and j was represented by yij. When ILP chose this edge, yij = 1, otherwise yij = 0.The 13 selected genes can be mapped to PPI network to get the corresponding proteins and the interactions between them and other proteins. Each selected gene was connected with some other genes in PPI network by the edges. In ILP algorithm, edge between yij to ensure the result network has the largest weight. For the weight of edge, wij, here we use the Pearson coefficient of the gene expression values to represent the weight between proteins i and j.ILP tries to assign 0 or 1 for κB as starting protein and APP as ending protein ILP model was applied to formulate the signaling network. In the ILP algorithm, penalty parameter λ is a size control parameter that needs to be adapted manually. If its value is too large, the predicted signaling network will be enormous, otherwise it will be too small to catch the useful biological information. In our simulation experiment, after adapting from small value to large value, λ was determined as 0.65.Then using NF-We finally got a signaling pathway with several small subnetworks. This network is reconstructed by 45 genes including 13 selected significant genes and is shown in κB” represents the starting protein NF-κB, “(3) APP” represents the ending protein APP, “(1)” with the protein names denote the corresponding selected genes by the regress model, and “(2)” with protein names are selected by ILP to reconstruct the signaling pathways between NF-κB and APP. In order to analyze the biological functions of the pathways and subnetworks, the predicted result was mapped into its coding gene pathway network, and the online analysis website DAVID (http://david.abcc.ncifcrf.gov/home.jsp) was utilized to further understand their molecular biological functions to AD. In First of all, among the prediction results, there are 5 genes that have been confirmed as the AD virulence genes such as SNCA, CALM1, GSK3B, PSEN1, and APP which have been biologically demonstrated playing crucial roles in AD. Based on κB, the central transcription factor in driving the inflammatory response [κB has long been considered a prototypical proinflammatory signaling pathway, largely based on the activation of NF-κB by proinflammatory cytokines, such as interleukin-1 (IL-1) and tumor necrosis factor α (TNFα), and the role of NF-κB on the expression of other proinflammatory genes including cytokines, chemokines, and adhesion molecules, which has been extensively reviewed elsewhere [It was found that inflammation is a major mechanism of acute brain injury and chronic neurodegeneration . During response . NF-κB hlsewhere .κB, Notch, MAKP, and PSEN1 included in the main pathway of κB via interleukin-1 receptor-associated kinase (IRAK) and mitogen-activated protein kinase (MEKK1(MAP3 K)) dependent inhibition of NF-κB inhibitor (I-κB) [κB subunit)) can trigger Notch homolog 1 translocation-associated (NOTCH1 receptor) signaling pathway by inducing expression of Jagged1, ligand for Notch receptors [Recent studies have also found that Notch receptors in Notch signaling pathway regulate cell differentiation and function, and Notch1 has been shown to induce glia in the peripheral nervous system , 20. NF-r (I-κB) , 22. V-receptors , 24. NOTeceptors .β-catenin- (CTNNB1-) dependent WNT signaling pathways have crucial roles in the regulation of diverse cell behaviours, including cell fate, proliferation, survival, differentiation, migration, and polarity [α function would inhibit Wnt/β-catenin signaling [β-catenin and NF-κB are independent pathways; cross-regulation between the Wnt and NF-κB signaling pathways has emerged as an important area for the regulation of a diverse array of genes and pathways active in chronic inflammation, immunity, development, and tumorigenesis. Both β-catenin and NF-κB activate inducible nitric oxide synthase (iNOS) gene expression [It was also found that  polarity , 27. It ignaling . Recentlpression .β in the brain are central to the pathogenesis of AD. COPS5 is found to be a novel RanBP9-binding protein that increases APP processing and Aβ generation [κB were found to be associated with them. Ubiquitin can degrade the IκB which is the inhibitor of NF-κB, processing of precursors, and activation of the IκB kinase (IKK) through a degradation-independent mechanism [In addition, the regulatory network between COPS5 and CENPC1 has been extracted from our algorithm which is also observed to be implicated in the pathogenesis of AD. The COP9 (constitutive photomorphogenesis 9) signalosome (COPS), a large multiprotein complex that resembles the 19S lid of the 26S proteasome, plays a central role in the regulation of the E3-cullin RING ubiquitin ligases (CRLs). The catalytic activity of the COPS complex, carried by subunit 5 (COPS 5/Jab1), COPS-dependent COPS 5, displays isopeptidase activity; it is intrinsically inactive in other physiologically relevant forms . Increasneration . COPS5 rneration . CENP-T neration . Our resechanism . On the echanism .β. However, the signaling pathways and regulatory networks of the inflammation in AD pathogenesis are very difficult to reconstruct due to the complexity.Although many efforts have been done several decades of AD, it is still difficult to uncover its phenotype-pathway relationship and pathogenesis. Recent studies show that the pathology of AD has an inflammatory component that is characterized by upregulation of proinflammatory cytokines, particularly in response to AκB and AD virulence gene APP since NF-κB has long been considered a prototypical proinflammatory signaling pathway. From the molecular biology analysis, we found that genes on the main pathway of the reconstruction results play crucial roles in inflammatory response and APP which give more biological insight for AD pathogenesis, such as NF-κB, NOTCH1, CTNNB1, COPS5, and their signaling pathways. Even more, the pathogenic contribution of the inflammatory response in AD is supported by our finding of the regulating and functions of the genes and subnetworks in the predicted signaling pathways. In general, our studies on combining PPI and gene expression data discover the signaling pathways of inflammatory response on AD and help for deeply understanding the pathogenesis of AD.To discover the inflammation signaling pathway and regulatory network of AD, in our study, protein interactive network data, PPI was introduced to overcome the information insufficiencies of DNA microarray gene expression data by integer linear programming (ILP) method. Two stages had been used in predicting inflammatory pathway for AD. Firstly, significant genes had been selected by linear regression analysis with the modified network-constrained regularization analysis. Then ILP model was applied to reconstruct the signaling pathway between NF-


Common approaches to pathway analysis treat pathways merely as lists of genes disregarding their topological structures, that is, ignoring the genes' interactions on which a pathway's cellular function depends. In contrast, PathWave has been developed for the analysis of high-throughput gene expression data that explicitly takes the topology of networks into account to identify both global dysregulation of and localized (switch-like) regulatory shifts within metabolic and signaling pathways. For this purpose, it applies adjusted wavelet transforms on optimized 2D grid representations of curated pathway maps.H. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli. We show that PathWave is more sensitive than common approaches and apply it to RNA-seq expression data, identifying crucial metabolic pathways in lung adenocarcinoma, as well as microarray expression data, identifying pathways involved in longevity of Drosophila.Here, we present the new version of PathWave with several substantial improvements including a new method for optimally mapping pathway networks unto compact 2D lattice grids, a more flexible and user-friendly interface, and pre-arranged 2D grid representations. These pathway representations are assembled for several species now comprising D. melanogaster. The PathWave R package is freely available at http://www.ichip.de/software/pathwave.html.PathWave is a generic method for pathway analysis complementing established tools like GSEA, and the update comprises efficient new features. In contrast to the tested commonly applied approaches which do not take network topology into account, PathWave enables identifying pathways that are either known be involved in or very likely associated with such diverse conditions as human lung cancer or aging of  Large-scale gene expression profiling by microarrays or transcript sequencing enables identifying relevant expression changes in cells by comparing gene expression patterns of two distinct conditions . A frequent goal of such studies is the identification of dysregulated cellular pathways involved in an observed phenotype like, for example, abnormal proliferation and migration of cancer cells.E. coli[For this, gene set enrichment tests are commonly applied on large scale gene expression profiles, testing over-representation of up- or/and downregulated genes in pathways. While most gene set enrichment approaches used for pathway analysis ignore topological information, we have introduced a computational method (PathWave)  that expE. coli.Here, we present the new, entirely revised and refined version of PathWave with several substantial improvements:i. a faster and more efficient arrangement of 2D grid representations of pathway networks (see below),H. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli,ii. pre-arranged 2D grid representations for several species, including iii. compliance with version 0.7.1 of the KEGG Markup Language (KGML),iv. an interface to obtain colored KEGG pathway maps depicting the identified expression changes,v. the use of pathways extracted from the Human metabolic model reconstruction  from thevi. performance improvements due to faster processing of expression data, andvii. a more flexible and user-friendly interface.Drosophila. Finally, we show that PathWave is more sensitive than the frequently used methods Gene Set Enrichment Analysis (GSEA) [We briefly summarize the PathWave workflow, discuss the most important novelties of the new version and demonstrate its usage by two case studies: (i) we applied PathWave to RNA-seq expression data to identify metabolic pathways that play a role in lung cancer, and (ii) we analyzed microarray expression data to identify pathways associated with longevity of s (GSEA)  and DAVIs (GSEA) .et al. [The workflow consists of two major steps: the preprocessing of pathway information and the analysis of expression data. We briefly summarize the two steps :Aij}i,j ∈ V where V is the set of nodes and Aij = 1 if nodes i and j are joined by an edge. The sparse adjacency matrices (Aij = 0 for most pairs of nodes) are optimally rearranged and embedded into smaller 2D, regular square lattice grid representations, best preserving the neighborhood characteristics of the reactions by placing adjacent nodes (Aij = 1) of the original network as close to each other as possible on the lattice grid : To run a PathWave analysis which involves the use of wavelet transforms (see below), compact 2D grid representations of pathways are required. Curated pathway maps are translated into networks in which nodes represent metabolic reactions and edges link two reactions if one reaction produces a metabolite that serves as a substrate for the other. Edges in signaling pathways are derived from direct interactions of the corresponding signaling proteins. These networks are described by their binary adjacency matrices {Step 2 : To identify pathways that are differentially regulated between two conditions or subsets of gene expression data, PathWave first associates gene expression levels with metabolic reactions or signaling proteins, thus mapping the expression data of each individual sample onto the 2D lattice grids of the pathways  and switch-like behaviors within the pathways, which significantly affect only small subnetworks (high-pass filter). To overcome the rigidity of wavelet transforms, PathWave covers any possible combination of neighboring nodes by implementing a one-step frameshift concept that applies the transform to the original grids and to grids shifted by one row, one column, or by both. Finally, pathways are ranked according to their most discriminative expression pattern, being it at a fine (e.g. metabolic switch in a sub-network) or coarse grain resolution . This ranking is done by statistical significance which is obtained by repeated random sampling of the samples  . Namely,t-tests). Due to the identification of such potentially relevant patterns within pathways, PathWave can be more sensitive than common gene set enrichment approaches.It is to note that an expression pattern composed of multiple genes (regarding an entire pathway or a sub-network) may significantly discriminate between two conditions  even when the single genes do not appear to be differentially expressed after multiple testing correction   into a compact 2D lattice grid computes an embedding of G into the lattice grid with the minimum possible total Manhattan distance (edge length) of nodes  that are adjacent in G. The method is based on a novel integer programming formulation that models the pairwise distances between the nodes on the grid, instead of their absolute positions used in the previous version [duv}uv ∈ E is embeddable, if there exists a 2D arrangement of the nodes V on the grid such that the Manhattan distance of u and v is exactly duv for every edge uv.The current PathWave version uses a new, up to 10nstances . The newu,v} of adjacent nodes u and v, we set the distance variable xuvk to 1 if k is less than or equal to the Manhattan distance of u and v. We set it to 0 otherwise. Since for fixed u and v the number of variables being 1 is exactly the Manhattan distance of u and v, the latter can be written as the sum of all variables xuvk over k. To obtain the total length of a pathway's embedding  one has to additionally sum over all edges uv that constitute the pathway. This is done in the objective function (1) of the following integer programming formulation whose goal is to minimize the pathway's total length in the embedding:For every unordered pair {subject toH =  of G and all distance vectors d on H that are not embeddable, and tofor all sub graphs uv ∈ E, 1 ≤ k ≤ dmax - 1, where all x variables are required to be binary.for all x, as these constraints cannot be modeled with integer variables directly taking the distance values . Since their number is exponential, we do not add them to the model from the beginning, but separate them during the branch-and-cut algorithm. The constraints in equation (3) ensure consistency of the x variables: if the Manhattan distance is at least k + 1, then it must be also at least k. The maximum possible distance dmax can be set to the diagonal diameter of the lattice grid. As dmax is usually significantly lower in practice, it is estimated by our algorithm via an initial series of tests. Moreover, the branch-and-cut algorithm features a simple primal construction heuristic. It produces approximate solutions even for large instances.The objective function (1) minimizes the total edge length of the embedding. The constraints in equation (2) ensure that the optimal distance vector is embeddable. They are the reason for the choice of the binary distance variables PathWave 2.1 is compliant with the latest KEGG KGML version v0.7.1) and an additional Perl script can be used to translate Systems Biology Markup Language (SBML) files from the BiGG database .1 and an to compaWe made the PathWave user interface more flexible and easier to use. Now, running a PathWave analysis (step 2) requires a single function call in R, as shown in the following simplified example:pwres < - pathWave.runhttp://www.genome.jp/kegg/ to obtain graphical pathway maps with color-coded metabolic reactions or signaling proteins according to their up- or down-regulation:Expression data and the definition of the two sample classes, or conditions, can either be specified as file names or passed as R data.frame objects and/or factor objects. The preprocessed pathway information (from step 1) to be used is specified as well as the number of permutations to be performed for statistical testing. When using KEGG, the search can optionally be limited to metabolic pathways. Additionally, we provide a simple function to build URLs for querying the KEGG web server at urls < - pathWave.getColorKEGGMapURLsH. sapiens, M. musculus, D. melanogaster, D. rerio, C. elegans, and E. coli and pathways extracted from the human metabolic model from BiGG [For convenience, the R package of the new version includes seven sets of preprocessed pathways for six species: KEGG pathways for rom BiGG ,8.P < 1E-16), along with the citrate cycle . Notably, in contrast to most reactions of the two pathways, some key reactions were down-regulated as shown in Additional file FBP1 and FBP2 encode fructose-1,6-bisphosphatase 1 and 2 (EC 3.1.3.11) and showed significantly lower expression in tumor cells than in adjacent normal tissue . The reaction catabolized by these enzymes converts D-fructose 1,6-bisphosphate to D-fructose 6-phosphate and opposes the metabolic flux during glycolysis (gluconeogenesis). In turn, the inverse reaction catabolized by phosphofructokinase (EC 2.7.1.11) was upregulated to allow high glycolytic flux. Phosphoenolpyruvate carboxykinase  is the main control point for extracting metabolites from the TCA cycle for gluconeogenesis, and this enzyme was downregulated while lactate production was upregulated.Lung adenocarcinoma is the most common type of lung cancer, and lung cancer is the world-wide leading cause of cancer-related deaths in males . Here, wPIG-C: P = 1.46E-11 (Bonferroni corrected), DPM2: P = 7.04E-11, PIG-L: P = 0.005, PIG-W: P = 5.21E-8 and PIG-M: P = 1.02E-13 encoding enyzmes processing UDP-N-acetylglucosamine into GPI anchor (GlcN)1 (Ino(acyl)-P)1 (Man)1).We detected a less known but nonetheless interesting metabolic switch in lung adenocarcinomas, namely the increased conversion of tryptophan to kynurenine by tryptophan 2,3-dioxygenase EC 1.13.11.11) and indoleamine 2,3-dioxygenase (EC 1.13.11.52), both of which were upregulated  and purine metabolism (P = 9.80E-3), and only six pathways had a nominal (uncorrected) P-value of less than 0.05  Starch and sucrose metabolism , M. musculus (mouse), D. melanogaster (fly), D. rerio (zebrafish), C. elegans (worm), and E. coli. The KGML files were preprocessed to build pathway networks in which nodes represent metabolic reactions or signaling proteins (depending on the type of pathway) that are linked by an edge if one reaction produces a metabolite that serves as a substrate for another or one signaling protein interacts with another, respectively. For each of these pathway networks an optimally arranged 2D grid representation was produced as described in Results.KEGG pathways  were extThe compartmentalized version of the Human recon 1 metabolic model  was obta2O) were ignored. Roughly, metabolites were excluded if they were involved in more than eight reactions. The final list was manually curated to (i) retain some specific, informative metabolites even if they were involved in more than eight reactions; and to (ii) exclude additional uninformative metabolites even if they were involved in eight or less reactions. Additional file To focus on functionally relevant interactions, several exceedingly common metabolites . Samples were selected at two time points  and for three adult diets . For more details, see Doroszuk et al. [For this case study, we used a very recent expression data set for anogaster  random shufflings of phenotype labels  for statistical testing. Final pathway P-values were corrected for multiple testing using the Bonferroni correction. For the lung cancer case study, we performed the PathWave analysis on metabolic pathways from KEGG and the human metabolic model from BiGG. Having more samples and thus more statistical power, we additionally required at least 10 genes/reactions to be up- or downregulated for a pathway to be selected and lowered the threshold of the Bonferroni corrected P-value from 0.05 to 0.001.For the To compare the sensitivity of PathWave with other, common approaches we used the RPKM expression values of 77 lung adenocarcinomas and their matched normal tissue, which was the larger dataset of our case studies.P-value < 0.05) which were tested for significant pathway enrichment using DAVID (http://david.abcc.ncifcrf.gov/). The background was the set of all 20,817 genes we had used for the PathWave analysis. The functional enrichment analysis was performed with default parameters. Results were extracted only for metabolic KEGG pathways and P-value correction for multiple testing was applied accordingly. For GSEA (http://www.broadinstitute.org/gsea/), we used only annotations from metabolic KEGG pathways. Statistical significance was evaluated by comparison with 10,000 random shufflings of phenotype labels  of the 154 samples.We identified 4,078 significantly upregulated and 3,065 significantly downregulated genes : Platform independentProgramming language: R, C++, PerlOther requirements: For running PathWave on expression data: R version 2.14 or higher, standard CRAN R packages , standard Bioconductor R packages ; for embedding pathways into compact 2D lattice grids: CPLEX, GridArranger v1.0 (available from the project homepage), ABACUS v2.4-alpha (available from the project homepage).License: GNU GPLAny restrictions to use by non-academics: noneThe authors declare that they have no competing interests.RK, RMP and RE designed the study; SW, MO and GR designed the new grid optimization method; SW and NR implemented the new grid optimization method; RMP and GS implemented the new PathWave R package; RMP performed the data analysis; RMP, RK and SW wrote the manuscript. All authors read and approved the final manuscript.Supplementary text, figures and tables.Click here for file


The Human Toxome Project, funded as an NIH Transformative Research grant 2011–2016, is focused on developing the concepts and the means for deducing, validating and sharing molecular pathways of toxicity (PoT). Using the test case of estrogenic endocrine disruption, the responses of MCF-7 human breast cancer cells are being phenotyped by transcriptomics and mass-spectrometry-based metabolomics. The bioinformatics tools for PoT deduction represent a core deliverable. A number of challenges for quality and standardization of cell systems, omics technologies and bioinformatics are being addressed. In parallel, concepts for annotation, validation and sharing of PoT information, as well as their link to adverse outcomes, are being developed. A reasonably comprehensive public database of PoT, the Human Toxome Knowledge-base, could become a point of reference for toxicological research and regulatory test strategies. The testing of any single chemical has become expensive, time-consuming and exorbitant in the use of animals . It is d (TT21c) . The repsciences  and risksciences  outlinedsciences .Within a year, the EPA, the National Toxicology Program (NTP) at NIEHS and the National Chemical Genomics Center (NCGC) announced a collaboration (Tox-21) to implement the key recommendations of the report . In 2007Simultaneously, EPA has been working on exposure prediction models (ExpoCast) for thousands of chemicals based on manufacture and use information . Togethein vitro test results and then using this in vitro toxicity information directly for setting regulatory standards. Second, the emphasis is on learning by doing. Many key issues relevant to the use of in vitro toxicity pathway assays for safety assessment will become apparent after completing the first two or three case studies. Most of the issues will become clear and expansion to other pathways will move along more quickly.Work at The Hamner Institute has utilized a number of case studies to identify the steps needed for immediate, wholesale changes to current practices . First, in vitro assays, estimates regions of exposures that should not cause excessive pathway perturbations in exposed populations. The definition of excessive perturbation will require in vitro assays that provide read-out at differing levels of severity and the ability to differentiate compensatory from adverse responses. Dose-response assessment from these assay results require integration of multiple data streams to infer the structure of the signaling circuitry and its dynamic response with increasing levels of perturbation. Dynamic, dose-response characteristics of toxicity pathways provide the grist for completing computational systems biology pathway models to assess shapes of dose response curves at low exposures -level systematic work under the heading of Adverse Outcome Pathways (AOP). The concepts of an AOP framework initially emerged at OECD in the context of ecotoxicology , but werAOP is largely a framework for referencing and assembling existing scientific knowledge into putative pathways. The goal of pathways of toxicity (PoT) , in contIncreasingly, technologies enabling broad biological phenotyping of the responses of cells and organisms are emerging that allow elucidating mechanisms of toxicological effects without the necessary prejudice of prior knowledge. These include the various omics and high-content imaging technologies . These iin vitro systems) and omics technologies. The bioinformatics tools for identification, annotation, proof of causality/validation, and link to adversity of PoT are not yet available.A number of challenges are discussed below. The concept of PoT itself is a hypothesis, i.e., are there a limited number of pathways which are conserved between cell types, organ systems and even species, as well as characteristics for toxicant classes and hazard manifestations? Challenges include the quality and standardization of the toxicological test systems , is in its third year of work. The project is unusual in that it is only developing many of the concepts to address these challenges while exploring and further developing, in parallel, the necessary technologies. A number of workshops and commissioned white papers complement the technical work. From the beginning, the project achieved high visibility , includiin utero through puberty and beyond, as well as effects on reproductive tissues and the development and progression of cancer, especially breast cancer. Environmental chemicals capable of estrogenic endocrine disruption include various organic pollutants such as polychlorinated biphenyls (PCBs), pesticides, dioxins, aromatic hydrocarbons, and various natural chemicals (such as genistein). In particular, there is great public concern about bisphenol A (BPA). Some studies in animal models have shown effects of low dose in utero exposure to xenoestrogens (such as BPA) to be associated with abnormal fetal reproductive tract development in male and female offspring  is widespread and testing for their effects is a high priority for regulatory agencies. The possible effects include altered development ffspring  and mammffspring . It remaThe exact mechanisms through which xenoestrogens affect biological systems are not clear. Estrogenic effects on gene transcription are mediated, it has been thought, by binding to the nuclear estrogen receptors alpha (ERα) and beta (ERβ). More recently, splice variants of ERα66, ERα36 and 46 and ERβ variants ERβ1–5 were identified . Furtherin vitro systems using fluorescent reporters (The estrogenic activities of many compounds have been examined in eporters  and celleporters . These aeporters  and metaeporters . Howevereporters .Endocrine disruption was chosen as the pilot for the human toxome because of the urgency to complement current risk assessment approaches  and the http://humantoxome.com) aims to map PoT using estrogenic endocrine disruption (ED) as a test case. The central goal is to define, experimentally verify and systematically annotate ED PoT. The overall strategy is to use omics  to map and annotate PoT, to develop software and visualization tools for integration and analysis of the multi-omics data streams, and to identify, annotate and validate PoT. The project will develop a consensus framework and a community database enabling toxicologists to map the human toxome. The components of the project are illustrated in The project “Mapping the Human Toxome by Systems Toxicology”  are made accessible to the consortium via a centralized cloud server. The last two years of the project include the establishment of the Human Toxome Knowledge-base.in vitro endocrine disruptor assay  to target ERα specific pathways and diarylpropionitrile (DPN) to target ERβ. In this system, MCF-7 cells demonstrate high responsiveness to treatment with PPT, with results reproducible between labs, but low to no response to the ERβ agonist DPN. This lack of ERβ activation may be due to the low level of ERβ in the MCF-7 cells. Ongoing work focuses on the ERα pathway and on generating samples for pathway mapping for common estrogenic endocrine disruptors with relevant human exposure, including BPA, genistein, zearalenone and nonylphenol. Additionally, work is moving forward with the T47D cell line, focusing on the ERβ pathway.in vivo state in many aspects – we take cells out of their natural environments , which we reproduce poorly in culture. We select the more robust and adaptable cells by providing non-homeostatic conditions favoring growth over differentiation  guidance  framework of OECD for regulatory studies gives only limited guidance for in vitro work . It is important to evaluate the corresponding changes in transcripts and metabolites at different time-points, doses/concentrations, and associated toxicological effects. Metabolomics measures the small molecules of metabolites (which are closest to the phenotype) and therefore has a wide application in toxicity studies . HoweverThe new “omics” era challenges us to put huge amounts of data from different platforms together to interpret them in the context of biology, but the lack of adequate bioinformatics tools is a bottleneck. New open source software and commercial solutions for multiomics data integration, however, are becoming available and facilitate the elucidation of PoT . This syTranscriptomics is one of the major omics approaches for mapping PoT. Transcriptional responses to exposure by environmental xenobiotics can be integrated with other omics approaches to develop an understanding of PoT . In thisin vitro omics data depends heavily on the quality of the test system and the analytical methods  and Meeting these challenges involves developing novel bioinformatics approaches and applying them to various high-throughput data streams from the literature and from experiments. Transcriptomics has been in use for over two decades and has well-established analysis tools and standards for best practices and documentation. For metabolomics, the analysis is a nascent field. Measuring the abundance of metabolites has technical challenges as well as data analysis bottlenecks due to relatively under-developed computational methods and data infrastructures. In particular, the relatively sparse annotation data available for metabolites compared to genes will require an approach that can infer networks from data  rather than depend only on curated pathway maps , as wellAs with all cell systems (and particularly for MCF-7 cells), one challenge is the variability of the system and the reproducibility of high-throughput results. MCF-7 cells show sensitivity of cellular responses to culture conditions, treatments and other factors, compounded by differences in normalization and analysis methods  but, importantly, we found higher correspondences at the pathway level. This does, however, indicate that one key approach is the establishment of an effective dimensionality reduction of the data so that the noise from both the biological and technical variability does not overwhelm the signal. This will ensure that the derived PoT is not the result of over-fitting to one model or one dataset and that it is robust enough to be expanded to all existing data .2 offers a platform to enable simultaneous analysis of transcriptomic, metabolomic, proteomic and several sequencing data types as well as mapping high-throughput data onto curated pathways from popular third-party sources. A unique combination of cutting-edge research tools and customized commercial software will allow PoT mapping to interrogate the data much more intuitively and robustly than using flat representations. Lastly, comprehensive PoT mapping requires robust, transparent, and flexible tools to maintain extensive metadata, QC attributes, primary and derived datasets, and the analysis results generated by the project. As a part of the effort to develop commercial software tailored to the needs of large-scale PoT mapping, the Human Toxome Consortium is developing a tailored Laboratory Information Management System based on Agilent’s OpenLAB ELN, which will be tightly integrated with toxome data analysis software via a dedicated software bridge.Because of its multidimensional nature, managing and visualizing data produced in a PoT mapping effort is challenging. Interactive visualization tools  are usefThe implementation of a more mechanistic approach to regulatory toxicology will require scientific data to be delivered faster, more efficiently and accessibly to regulators and advisory bodies . The roaExisting knowledge is scattered in several scientific disciplines and throughout many publications and databases. Although the scientific community has seen a proliferation of pathway-oriented databases such as KEGG, WikiPathways and Reactome as well as several chemical-centered databases such as ACToR, Toxnet or the Comparative Toxicogenomics Database, these databases are poorly harmonized and links between them are rare. As a result, the content is fragmented, appears in multiple formats, and the databases are developed mostly independently of each other. A comprehensive source of information – from adverse outcomes to molecular targets to chemical structure of a toxicant – does not exist, nor are high-throughput data vetted for reliability with, e.g., Klimisch scores . The exiOne key consideration is the necessity of making the data accessible for bioinformatics approaches – and that requires making use of ontologies ,b such aLastly, it is critical to ensure that any knowledge-base maintains links and integration to other specialized databases  in order to leverage data and be useful to the widest possible audience – and this means ensuring data portability between databases. While this problem has been largely solved for transcriptomics data, other omics approaches suffer from a relatively poor infrastructure, e.g., metabolomics .Traditional risk assessments set regulatory limits based on points of departure (PoD) from apical responses seen in animal studies by applying either linear extrapolation to zero-exposure levels or using multiple safety factors. Even then, there remains considerable debate regarding adaptive/reversible effects and adverse responses. One fundamental challenge for a TT21c strategy is to distinguish stress-related adaptive versus adverse responses at a cellular level. Adaptive responses are reversible cellular alterations, while adverse responses result in long-lasting cellular effects or new susceptibilities. The distinction becomes the tipping point for a regulatory action, such as setting an exposure limit for a chemical. At some point in the dose-response, the protective cellular responses at various levels of organization  will be overwhelmed with failures aligning to produce an “adverse” effect. This failure, commonly known as the “Swiss cheese model of error analysis”, may provide a framework for investigating the cellular dose-response datasets .in vitro assays against in vivo mechanistic studies will be valuable to gain confidence in the value of cell-based approaches . In the short-term some level of quality assurance of proaches .in vitro. Bioinformatic and pathway analysis of the large datasets will lead to the identification of cellular responses (both protective and harmful) that are repeatedly observed in progressive cellular injury , US (ICCVAM/NICEATM), Japan (JaCVAM), Korea (KoCVAM), and Brazil (BraCVAM). Some adaptations to in silico approaches have taken place . Both frameworks are helpful in establishing whether PoT are derived from a meaningful test system and whether a given analyte is predictive for a certain health effect. Neither addresses the aspect of causality or the confirmation of a chain of events leading to adverse outcome, however.Validation has been developed  and inteen place , but no Validating a PoT means establishing the causality between toxicant and hazard manifestation and identification of how this happens. This is difficult on the level of correlation, because we typically do not have the human data for a statistically sufficient number of substances. However, we have growing knowledge of the mechanisms relevant to human health effects. Thus, the efficacy to cover relevant mechanisms for human health and environmental effects is becoming increasingly important. We have called this “mechanistic validation” . MechaniThe first goal of validation is to demonstrate reproducibility; it should not be a major problem to translate this to PoT. It would require first demonstrating the reproducibility of the results that led to PoT deduction. Arguably, the reproducibility of PoT involvement for a number of toxicants for which the same PoT is claimed needs to be shown. Furthermore, this might require demonstrating that PoT involvement can be shown by orthogonal techniques.The problem of validation of new approaches such as PoT is the lack of a point of reference to establish the relevance of the approach. Traditionally, an animal experiment has been chosen as point of reference – which also is problematic . HoweverWe are facing a series of challenges in addition to finding a technical solution, especially for PoT mapping, to put the vision of TT21c into practice . These iin vitro to in vivo extrapolation. These need to be put together again in a way that allows decision-making, ultimately envisioned as Systems Toxicology by simulation  and adding safety (assessment) factors of typically 100–1,000 for possible differences from animals to humans). This approach has arguably protected us despite the introduction of thousands of substances . Substances are mainly tested for acute and topical effects, however, and we do not know how well this has protected us against chronic and systemic effects, as only drugs are followed-up for possible health effects when they are on the market.The explosion of knowledge in life sciences and new technologies with systems level data makes it attractive to use mechanistic approaches. So far, these approaches have been limited to species-specific toxicity – to make the case that certain effects observed in animals are not relevant to humans in order to pursue a product.The principal question arises: What is a toxicological mechanism? The question is simpler than the answer, because we are looking into highly dynamic networked systems. It is difficult to distinguish where normal response, defense, and adversity start. Most substances are promiscuous in the sense that they have not only one target for interaction with the biological system (molecular initiating event) but they will perturb more and more downstream pathways with increasing dose or duration of exposure. It is difficult to identify which is the pace-making  pathway of a hazard manifestation. Further complications arise from the fact that an organism is a moving target, i.e., developmental processes, adaptive processes, cyclical processes, aging and degeneration all interfere with the perturbation under study. This is especially problematic if we look for the more subtle effects of low-dose chronic exposures. Next, it is not clear how much variance we face: Are these pathways sufficiently conserved between cells, species, or for a given group of toxicants employing the same mechanism? Last but not least, will we still see the PoT at work when dealing with the real-life exposures to mixtures?All these questions can only be answered by simply doing it. Many iterations and refinements will be necessary. Only when we have a first PoT annotated will we be able to see where and when it works and whether its definition needs to be adapted. Every journey starts with the first step, and these are the first humble steps toward a human toxome. Many more similar and hopefully coordinated projects will be necessary to complete the journey.


Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity.Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant.We defined functional profiles of Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. Saccharomyces cerevisiae to model functionality [Biological functions must be carried out in a synchronised manner to ensure proper timing of processes like cell division and metabolism. Molecular functions arise from complicated sets of physical interactions between large numbers of proteins, RNAs and various regulatory pathways, which can be difficult to reconstruct, represent and analyse. In systems biology, molecular function is mapped using molecular interaction networks. Protein-protein interaction (PPI) networks are frequently used to map protein functionality -5. Withiionality -11 due tA great deal of research has focused on computational methods used to identify clusters/sub-networks based on topological features -14. HoweTo deal with the issue of spatial/temporal edges we propose a method using experimentally validated pathways as the units of cellular processes. In this context pathways represent groups of proteins shown to interact under specific experimental conditions. This differs from the definition used in Kelley (2005) , in whicGene annotation data was integrated with pathway data to produce a set of annotated pathways, which were assembled into a functional network and analysed. An outline of the methods is given in Figure S. cerevisiae pathway names and their constituent genes/proteins were retrieved from ConsensusPathDB (CPDB) ([ (CPDB) , instances of other GO terms in the pathway, instances of the annotation outside the pathway, and instances of all other GO terms outside the pathway. Using an enrichment score of 0.01 as the threshold for allocating GO terms, annotations are assigned at 99% specificity. Rather than correcting for multiple testing, we use later processing stages to remove false positive annotations, which are designed to be flexible to the varying specificity of GO term-pathway relationships. P-values gained from Fisher's exact tests are therefore referred to as enrichment scores.The functional profile of a pathway is defined as a reduced set of enriched GO annotations that give maximum representation of a pathway's genes. Enriched annotations that were only present in one gene/protein within the pathway were excluded, as they are likely to be spurious and give a poor representation of the pathway's function.The remaining annotations in each pathway's enrichment profile were considered for inclusion in the functional profile, by the ranking of their enrichment score (lowest enrichment scores first). The first GO term is selected and checked against the annotations of each gene/protein in the pathway Figure . Genes aPleiotropy describes genes that contribute to more than one phenotype, implying that the gene/protein is involved in more than one function. This may be due to presence of the gene/protein in different pathways, or the genes within a single pathway affecting multiple functions , resultiWithin functional profiles, the median semantic distance between pairs of GO terms was 6 and 95% of GO term pairs had semantic distances above 11.2. Therefore a semantic distance of 11.2 was used as the measure of pleiotropy. To avoid false positive annotations, GO terms from enriched profiles were only considered pleiotropic if they had an enrichment score below 0.0005. The semantic distance between each GO term in each pathway's enriched profile and all the GO terms in the functional profile was measured. Any enriched annotations that had a distance greater than 11.2 from all of the GO terms in the functional profile, were considered pleiotropic and added to the functional profile. Using these parameters 32 GO terms were added to 25 pathways.A concern when adding pleiotropic terms was that large semantic distances may be more likely to arise in larger pathways with more genes, resulting in less specific pathway functions. Plotting the number of GO annotations versus maximum semantic distances between annotations in enriched profiles . Jaccard coefficient scores were used to weight the edges in an undirected network.where A and B are two sets of GO terms.T) of 0.8 (Equation 2). We calculated pairwise similarity scores (abS) between GO terms (a and b) of pairs of pathways (A and B), retaining only scores below T. The retained similarities were normalised, then summed to give a value (ABV) expressing the total similarity between the annotations in both pathways . The resulting value was then divided by the number of possible GO term pairs, to obtain the edge weight (ABW). GO term pairs with scores below the threshold of 0.8 represent the most extreme cases of semantic similarity (<0.1% of semantic distances), ensuring that the majority of the edges in the network represent identical shared annotations (74%).Due to the size and hierarchical nature of GO it is possible that multiple annotations may describe very similar cellular functions. Pathways with different annotations describing highly similar functions would not be linked, therefore the network would fail to represent the pathways' functional similarity. To overcome this issue, links have been created between nodes with semantically similar annotations below a threshold . Removal of duplicated pathway names and gene sets, as well as pathways containing fewer than three genes, reduced the number of pathways in the data set to 553 . This is in part related to the hierarchical nature of the Gene Ontology, implying that many of these annotations are describing a small number of functions at various levels of detail. Functional profiles were created to give a succinct representation of each pathway's specific functions, by selecting a reduced set of GO terms to describe the maximum number of genes/proteins inside each pathway . Only 35% of pathways were described by a single GO term, demonstrating that functions defined by the Gene Ontology cannot be directly mapped onto pathways, as the relationship is more complex. A moderate correlation was found between the number of genes/proteins in a pathway and the number of GO terms in its functional profile (coefficient 0.5). The majority of pathways had unique functional profiles, however 13% of functional profiles were not unique to a pathway indicating that some GO functions may be shared by discrete groups of pathways.The functional profile algorithm (see Methods) selects the most enriched annotations for genes/proteins within the context of each pathway; however, multiple functions performed by genes/proteins may be missed. As a result of incorporating pleiotropic terms, 32 additional annotations were added to 25 pathways, with each pathway receiving between one and three terms. Examples of the information added by including pleiotropic terms are given in Table We analysed the semantic distance between GO terms co-occurring within functional profiles Figure . The disMultiple functions can be distributed across the genes/proteins within a pathway in three ways. Functional profile annotations are either distributed across overlapping, discrete (disjoint) or pleiotropic sets of genes within the pathway  and removed from the data set.Click here for fileFigure S1: Network clusters created using ONECLUST. The Cytoscape plugin ClusterONE was used to calculate network clusters, using weighted edges and a minimum cluster density of 0.25 to include all the main clusters. (*.pdf).Click here for file


The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes  can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at:  Diagnostic strategies are rapidly changing in cancer and other diseases because of the availability of increasingly affordable genomic analysis . TherapiHere we present PathAct, a web server that assess how interventions over genes  can affect to signaling pathways and, ultimately, to the cell functionalities triggered by them. PathAct implements improved robust models of signaling pathways, taken from KEGG  and basePathAct uses the measurements of gene expression in a given condition  as reference and calculates the signaling activity of all the signaling circuits represented in the pathways. Then, within the interactive workspace, users can easily make interventions on this reference condition. Interventions consist on gene deactivations (simulating KOs) or gene activations (simulating over-expressions). The consequences of these interventions are calculated and the resulting condition is compared to the reference condition, highlighting the differences.By default, PathAct is used in anonymous mode, which implies that after the session is finished, the results obtained are lost. Alternatively, users can choose sign up for an account. The functionalities are exactly the same but the all the results are stored in the server and available for future sessions.Jobs panel . Such value constitutes its contribution to the signal transmission that could be very relevant if it is a bottleneck for the signal or irrelevant if, for example, lies in one branch of a redundant bifurcation. In addition, the gene can be present in more than one pathway which means that it can be contributing to several signaling circuits simultaneously. The value prompted can be modified to simulate interventions. The selected genes appear in the Add genes panel . Since gene activity values must range between 0 and 1, activity values over 1 after the application of an agonist weight are trimmed to 1. The intensity of the effect produced by the drugs can be modulated in the Configure Drug Action Weights window invoked using the Settings option of the main menu.Also the effect of drugs with known targets (as described in DrugBank) over the different signaling pathways can be studied. The x Figure  allows sl Figure . Additiol Figure  displaysl Figure . If the n Figure  is pressSince many genes participate in more than one pathway and many drugs affect to more than one gene but, simultaneously, signaling circuits are wired with a substantial level of redundancy, the predicted results of drug effects are often unexpected. This fact highlights the importance of comprehensive holistic simulation approaches like the one presented here.Obviously, unknown off-target effects cannot be predicted. However, they could eventually be inferred by comparing the predicted condition to the real condition measured upon the application of the drug.http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/021923s016lbl.pdf). We use the data in the program example, taken from the TCGA (ID: TCGA.BH.A1FM.11B.23R.A13Q.07), which corresponds to a woman with breast cancer, aged 44, that died 1388 days after diagnostic. Once the data are ready, we choose Sorafenif in the Add drugs search box  and using CellMaps (http://cellmaps.babelomics.org) functionalities. The backend, which makes the calculations of pathway activities, is written in R. Drugs that target the selected genes are taken from DrugBank  effect that interventions on one or several genes have over all the signaling circuits in all the pathways. Moreover, since signals received by receptor proteins trigger functions mediated by the effector proteins at the end of the signaling circuits, both the direct and the long-distance functional consequences of interventions over the genes of the circuit can be straightforwardly revealed within this actionable pathway scenario. PathAct actively contributes to save an enormous amount of time and resources in trial-and-error experiments by allowing highly focused testing of hypothesis of intervention over a reduced number of signaling circuits. In this way this tool will decisively contribute to the acceleration of the discovery of new drug targets and can enormously facilitate application of targeted therapies in personalized treatments. To our knowledge, there are no other tools available that offer a similar functionality.


The aim of the present study was to screen the differentially expressed genes (DEGs) associated with familial combined hyperlipidemia (FCHL) and examine the changing patterns. The transcription profile of GSE18965 was obtained from the NCBI Gene Expression Omnibus database, including 12 FCHL samples and 12 control specimens. The DEGs were identified using a linear models for microarray data package in the R programming language. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also performed. Protein-protein interaction (PPI) networks of the DEGs were constructed using the EnrichNet online tool. In addition, cluster analysis of the genes in networks was performed using ClusterONE. A total of 879 DEGs were screened, including 394 upregulated and 485 downregulated genes. Enrichment analysis identified four important KEGG pathways associated with FCHL: One carbon pool by folate, α-linolenic acid metabolism, asthma and the glycosphingolipid biosynthesis-globo series. GO annotation identified 12 enriched biological processes, including one associated with hematopoiesis and four associated with bone cell differentiation. This identification was in accordance with clinical data and experiments into hyperlipidemia and bone lesions. Based on PPI networks, these DEGs had a close association with immune responses, hormone responses and cytokine-cytokine receptors. In conclusion, these DEGs may be used as specific therapeutic molecular targets in the treatment of FCHL. The present findings may provide the basis for understanding the pathogenesis of FCHL in future studies. However, further experiments are required to confirm these results. Familial combined hyperlipidemia (FCHL), the most common genetic form of hyperlipidemia, is characterized by significant familial clustering and premature coronary heart disease . FCHL iset al , a nuclear factor member of the RXR superfamily, which is critical in lipid homeostasis  is a transcription factor, which regulates the expression of a number of genes involved in glucose and lipid metabolism, and provides an adequate candidate for FCHL . PrelimiIn the present study, the aim was to analyze the FCHL samples and control samples with a series of biological information technology services, with the purpose of revealing the mechanism underlying the development of FCHL. Gene-set enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed. Functional genes and signaling pathways in FCHL were used to establish a theoretical foundation for future research. Present findings may provide a basis for understanding the pathogenesis of FCHL in the future.http://www.ncbi.nlm.nih.gov/geo/), which is based on the Affymetrix Human Genome U133A Array GPL96 . There were a total of 24 RNA specimens (lymphoblastic cells), including 12 FCHL samples and 12 control specimens.The transcription profile of GSE1010 was obtained from the NCBI Gene Expression Omnibus (GEO) database (The linear models for microarray data package  in the Rhttp://www.enrichnet.org/) (http://www.string-db.org/)  . EnrichNdb.org/)  databasehttp://cytoscape.org/)   and thenClusterONE is a graph-clustering algorithm, which is used for forecasting the potential protein complexes in the weighted PPI . The weihttp://www.ebi.ac.uk/interpro/)   databaseGO gene annotation of the PPI network was performed via EnrichNet. GO terms were classified into biological process (BP) and molecular function (MF). The Pearson correlation coefficient was 0.8 and the threshold value for the XD-score was 1.68.To identify the specific DEGs between human FCHL tissues and healthy controls, the publicly available microarray dataset, GSE1010 was obtained from the GEO database. The gene expression profiling data were preprocessed using the Affy package and were normalized by the median method. At P<0.05, a total of 879 DEGs were identified, including 394 upregulated and 485 downregulated genes, of which the 10 most predominant upregulated and downregulated genes are listed in PPI networks of the DEGs were constructed via STRING. Subsequently, the KEGG pathway enrichment analysis of DEGs in the PPI networks was performed using EnrichNet. The PPI network included 431 nodes and 1,124 associations . The preThe PPI network of DEGs involved in the main four KEGG pathways is shown in At P<0.01, a total of 10 protein complexes were predicted via ClusterONE . The genComplex 6 revealed no KEGG pathway enrichment, but these DEGs were members of the G protein-coupled receptor (GPCR) family, of which 75% were localized in the cell membrane. Approximately 43% of the DEGs of Complex 7 were localized in the synaptic vesicles and the DEGs of Complex 10 were enriched in protein domains without the determined localization. It was difficult to determine whether Complex 7 and 10 may have biological functions. In addition, the function of Complexes 8 and 9 was not verified, which may be due to an error with ClusterONE.GO gene annotation of DEGs revealed 12 BP GO terms , includiThe present study used the EnrichNet online database to analyze RNA samples from patients with FCHL. Initially, the PPI network was constructed for DEGs, subsequently PPI and KEGG pathways were compared in the database (or GO gene annotation) and the KEGG pathway enrichment in DEGs (or GO terms) were identified. KEGG pathway analysis identified four important KEGG pathways, including one carbon pool by folate (hsa00670), α-linolenic acid metabolism (hsa00592), asthma (hsa05310) and the glycosphingolipid biosynthesis-globo series (hsa00603). The one carbon pool by folate pathway consists of the folic acid synthesis of folate THF biosynthesis and the C1-unit conversion process. THF, a carrier of the one-carbon group, acts as a coenzyme DNA-synthesis of nucleic acid and a lack of THF can lead to anemia . The α-lParhami  summarizet al (−/− bone marrow. The demonstration that Abcg1−/− macrophages undergo accelerated apoptosis provides a mechanism to explain the decrease in atherosclerotic lesions. Drechsler et al (GO function analysis identified 12 enriched BP terms, of which one term was associated with hematopoiesis and four terms were associated with bone cell differentiation. This finding was in accordance with hyperlipidemia and bone lesions in clinical and experimental settings. To date, clinical trials for the treatment of ischemic heart disease and heart failure using bone marrow cells have rapidly increased . Baldán et al  have demer et al  providedIn conclusion, the current study identified 897 DEGs and analyzed their functions. Additionally, bioinformatics methods were used to analyze the overlapping DEGs with known genes of the KEGG pathways. Subsequently, the enriched GO terms of DEGs were analyzed. The present study may provide a basis for improved understanding of FCHL. However, experimental studies are required to confirm these findings.