The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. They are caused by an interplay of several, often moderate genetic effects and environmental factors . Their Gene-set analysis methods facilitate the detection of associations between an individual's genetic information and a phenotype of interest, for example, disease status. The joint analysis of several genes often leads to increased power, as it reduces the overall number of conducted tests and assists in the detection of moderate associations . FurtherLarge sample sizes are required to detect weak genetic effects influencing disease risk. Thanks to technical advances and the formation of data-sharing consortia in particular, larger GWAS datasets have become available over recent years. However, genotyping and participant recruitment are still cost and work intensive. Especially in rare diseases, taking as an example the analysis of histological subtypes of a disease, it is very challenging to achieve sample sizes that result in adequate power in analyses . AnotherKernel methods in statistics have already been demonstrated as dealing well with the challenges faced when analyzing GWAS data , 9. TheyWe extend kernel-based analysis of GWAS data by integrating a network-based kernel function into a boosting framework, in order to identify genetic variation modulating disease susceptibility. Boosting emerged from the field of machine learning and was later transferred to statistical modelling. It implements an ensemble of many weak learners  to optimize the predictive accuracy of a model . Since iWe applied our approach to two GWAS datasets, one on lung cancer and one on rheumatoid arthritis. Lung cancer is one of the most common forms of cancer, especially in industrialized nations. It is responsible for the greatest proportion of deaths caused by cancer worldwide . AlthougIn  R [ =  separately for each effect in the additive predictor (um]).n ∈ {500,1000,2000} with a 1 : 1 ratio of cases to controls). Datasets for all scenarios were simulated for 100 replications. Note that these scenarios are small compared to typically available sample sizes nowadays. The reason can be found in the computational demands of the method for an insightful number of replications. Accordingly, comparably strong effects of markers were chosen to match the sample sizes used in our simulations.To evaluate the performance of kernel boosting, we conducted a simulation study based on simulated SNP data in combination with gene networks from existing biological pathways. Pathway information was extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) . For simmstop, we used 20-fold subsampling for each model on each of the datasets with a maximum number of 200 iterations. Using the network-based kernel function in both methods, we compared the results from our kernel boosting approach on multiple pathways to those obtained from the single-pathway LKMT [For each simulated dataset, we fitted a boosting model with pathway kernels. In order to tune the model, that is, to derive the optimal number of boosting steps way LKMT –11. Addi pseudogene to represent the gene within our simulations. Such a pseudogene was a randomly selected DNA segment on chromosome one of the reference data including five different SNPs. Between each two sampled regions, we ensured a distance of at least 100 kilo base pairs to prevent distortive LD correlations between them [ pseudogenes was left unchanged for all simulations, resulting in a realistic correlation structure for all simulation scenarios. In each of the 100 simulation runs, new genotype data for a total of 11,665 SNPs in 2,333 pseudogenes were simulated using the HAPGEN2 software. This software generates new haplotype data by combining a given set of reference haplotypes with previously simulated data. The detailed procedure is described in [All genotypes were simulated with the help of a reference dataset from the International HapMap Consortium . The refeen them . The locribed in .In the null case, noninformative genetic data were simulated for 1000 individuals. In each replication, new genotypes without association signals were generated for 11,665 SNPs. The disease status was assigned at random with 0.5 binomial probability of being a case, completely independent of genotype information. In each of the six effect scenarios, genotype data for a previously chosen equal number of cases and controls were simulated such that two pathways affected disease status. Association signals were included in three genes per causal pathway. In each of the resulting six genes, two randomly selected SNPs were chosen to be influential on the binary clinical outcome. Within one simulation scenario, all associated SNPs had the same effect strength and for each SNP the minor allele was influential. All effects were simulated as additive. To simplify the evaluation, we decided not to include environmental variables in these settings. hsa04020 and hsa04022) to include causal genes. In accordance with the findings in [We chose two typical pathways  with 488 cases and 478 controls, based on the data of participants taken from the following three individual studies: Lung Cancer in the Young (LUCY), a population-based multicentre study run by the Helmholtz Zentrum Munich, and the University Medical Centre of the Georg-August-University in Goettingen. This study includes data of lung cancer patients under the age of 51 and family members recruited in German hospitals [ospitals , 42. Theospitals . Additioospitals . A subseThe second GWAS is a rheumatoid arthritis study of the North American Rheumatoid Arthritis Consortium (NARAC). It includes 868 cases from New York hospitals, in which rheumatoid arthritis was diagnosed based on the criteria of the American College of Rheumatology. Additionally, 1,194 controls matching in self-reported ethnic background were collected. All individuals were genotyped with the HumanHap500v1 array , 46.For the rheumatoid arthritis study, we utilized gender as environmental covariate. In the lung cancer study, age and smoking exposure, measured in pack years, were also considered. To determine the pack year, one multiplies the number of packs of cigarettes smoked per day by the number of years an individual has smoked.R package biomaRt [All GWAS data were subjected to strict quality control. Only individuals with a genotype call rate of at least 95% were considered. SNPs with more than 10% missing values or with a minor allele frequency (MAF) below 0.1% were excluded from further analysis. Missing values in remaining markers were imputed with BEAGLE . No SNPs biomaRt , 50. Gen biomaRt .The KEGG database groups pathways in disjoint subsets according to their biological functionality. In the analysis of the rheumatoid arthritis and lung cancer data, we used a subgroup of 73 pathways connected to human diseases see . The infmstop was derived via 20-fold subsampling and the default step length of 0.1 was used. For the purpose of comparison, each of the pathways considered in GWAS data analysis was also tested individually on the corresponding data using the LKMT. The same environmental variables that were used in the offset model for boosting were also considered for the LKMT. Prediction accuracy was measured by the misclassification rate and the area under the ROC curve (AUC) for both datasets. Of note, prediction accuracy is influenced by the applied model but also by the dataset at hand, that is, the amount of information contained in the data. Additionally, we provided the cross-validation results, that is, the (average) negative binomial likelihood on the data that was not used for model fitting .For each pathway analyzed, the network-based kernel function with 4 degrees of freedom served as base-learner. The optimal number of iterations We compared the number of pathways each approach identified as associated with disease risk and considered the respective overlap in the results. The noninformative genetic data simulation comprised genotype data for 50 pathways and 1,000 individuals. p value below 0.05 (lighter grey bars) and those with p values below the Bonferroni-corrected significance level of 0.001 (darker grey bars) are indicated. Pathways containing influential genes are additionally highlighted in italics.Figures  hsa04020 and hsa04022, and the pathway hsa04610. It seems that the latter pathway is able to depict some of the information of the influential gene more effectively than the causal pathway for which it was originally simulated. This can be explained, as hsa04610 has the highest transitivity (0.14), also known as global clustering coefficient, of all simulation pathways and contains an effect gene. As the network kernel was designed to work especially well in detection of interconnected genetic effects, the causal gene is identified very well in the pathway when using this base-learner. Note that the same pathway did not stand out in the noninformative simulation scenario. Thus, we conclude that high transitivity facilitates the detection of causal effects when using the network-based kernel but does not lead to false positives . Several other pathways were also selected, but only with very low frequencies. In the same simulation scenario, the LKMT had very high power to detect the two pathways simulated to affect disease risk, however, also detected other pathways including any of the causal genes on the Bonferroni-adjusted significance level . No other pathway was selected. The misclassification error of the tuned boosting model for lung cancer  was 24.5% and the AUC was 0.785. The ROC curve and the cross-validation results are presented in the Supplementary Material 1, Section B. The LKMT with network-based kernel did not detect any associated pathway on the Bonferroni-corrected significance level. The prion diseases pathway appears ranked 20 out of 73 pathways, when sorting pathways according to ascending Bonferroni-corrected diseases . Neverthdiseases , 54. A fp values in As expected, analysis of the rheumatoid arthritis dataset discovered a variety of pathways (compare results in ). KernelWe extend a successful method for single-pathway tests to a multivariate selection approach for simultaneous analysis of several pathways. The resulting kernel boosting method benefits from the advantages of a kernel-based analysis, while at the same time overcomes some of the limitations inherent to testing procedures.p values, which only provide limited information on the relevance of a genetic effect. A more meaningful result would be an effect measure for the investigated trait or better still the ability to predict an outcome. Kernel boosting facilitates prediction, based on the selected influential variables, as was elucidated in the application where the overall prediction accuracy of each of the models was reported. Thus, it is also possible to interpret the influence of a specific genetic alteration by comparing the change in the predicted outcomes. A high degree of prediction accuracy for the model is ensured through the convenient evaluation of its performance on subsamples of the investigated dataset. This procedure usually results in good prediction accuracy and a sparse model.Moreover, our multivariable approach to GWAS data analysis does not provide Owing to the built-in shrinkage, our boosting approach is capable of dealing with correlated effects. Hence, correlated pathways, which partly include the same genes, can be handled within this framework. Thanks to the multivariable nature of the approach, only the best-fitting pathways, evaluated in terms of prediction accuracy, will be chosen to enter the model. Thus, only the pathway most clearly representing a particular genetic effect will be selected, depending on those pathways selected previously. Our observations support the statement by de Leeuw et al.  that comWe consider pathways as analysis units; however various other options exist. Single SNPs in transcribed or untranscribed regions, and SNP sets aggregated to represent a specific genomic region, environmental variables, or other variables, may be investigated and even combined arbitrarily within one model. For example, the application of our method to the genes comprising a pathway may help to identify key influential genes within the network  systems. However, such analysis of very large datasets places a rather high demand even on the most powerful HPCC systems to date. Usually, our kernel base-learners are based on the pairwise similarities of all observations. This leads to thm; see , 61, 62)Kernel boosting constitutes a new and potentially powerful tool in the analysis of GWAS data. It offers a highly flexible and extensible framework, suitable for a wide range of application scenarios. We account for the high complexity of possible gene interactions via the use of kernel functions, while reducing the complexity of the resulting model with the built-in shrinkage of the boosting approach. The resulting model enables us to predict traits and returns more meaningful results than a testing procedure. We conclude that kernel boosting is a suitable methodological addition for the analysis of GWAS, which supports the detection and interpretation of genetic risk factors influencing disease susceptibility.Supplement 1 : the supplemental text gives further details on the conducted analysis. It contains additional information on the simulation study including explanations on the choice of the maximum number of iterations, an in-depth analysis of the selection of relevant pathways, details on the computational requirements, and details on the effect pathways used for the simulation study. The supplement also contains further results of the data analyses on lung cancer and rheumatoid arthritis.Supplement 2 : the supplemental code and data files provide an exemplary application of the kernel boosting method to a simulated data set. A readme file and comments in the code highlight all important aspects and explain the analysis steps which are required.


Glioma has a poor survival rate in patients even with aggressive treatment. Temozolomide (TMZ) is the standard chemotherapeutic choice for treating glioma, but TMZ treatment consistently leads to high resistance.To investigate the underlying mechanisms of TMZ action with new therapeutic regimens in glioma.The biological effects of TMZ mainly depend on the three following DNA repair systems: methylguanine methyltransferase (MGMT), mismatch repair (MMR) and base excision repair (BER). Based on related genes in these three systems, web-based tools containing data compiled from open-source databases, including DrugBank, STRING, WebGestalt and ClueGO, were queried, and five common genes along with the top fifteen pathways, including the glioma pathway, were identified. A genomic analysis of the six genes identified in the glioma pathway by cBioPortal indicated that TMZ might exert biological effects via interaction with the tumor protein P53(TP53) signaling axis. Finally, a survival analysis with the six genes in glioma cases (low-grade glioma and glioblastoma multiforme) was conducted using OncoLnc, which might provide directions for the future exploration of prognosis in glioma.This study indicates that a functional network analysis resembles a “BioGPS”, with the ability to draw a web-based scientific map that can productively and cost-effectively associate TMZ with its primary and secondary biological targets. Glioma is the most common primary brain tumor in adults and has the highest degree of malignancy . CurrentO6 position of guanine [MGMT is a DNA repair protein encoded by the MGMT gene and is involved in the cellular defense against toxicity and mutagenesis from alkylating agents [The action of TMZ undergo DNA damage and DNA repair system two stages. In general, DNA repair systems are in charge of maintaining the genome integrity of cellular organism as they are able to neutralize DNA damage induced by various chemical and physical agents . Therefo guanine . Consequ guanine . During  guanine , 11. Thu guanine . (1) MGMg agents . (2) Theg agents . (3) Theg agents .Prior to the last decade, TMZ was shown to significantly improve outcomes in patients with GBM when administered concomitantly with radiotherapy and as maintenance strategy thereafter . NeverthWith the advancements in multicenter genomics studies, including microarrays, proteomics and high-throughput screening assays, exploratory network-based studies have been used to analyze massive amounts of data and numerous human diseases. In this study, we first employed DrugBank in a broad study of TMZ and drug-target information. Based on the three DNA repair systems, we identified five common targets using the STRING database. Then, the targets and associated genes were further explored through pathway enrichment analysis using the WebGestalt and ClueGO databases; the glioma pathway was screened, and related genes were identified to further explore genomic alterations using the cBio Cancer Genomics Portal  database. Overall, our study based on these functional network analyses provides valuable information for exploring the underlying mechanisms of TMZ action against glioma and for identifying potential targets to overcome TMZ resistance in glioma.DrugBank was first queried using TMZ as an input, which resulted in the output DB00853 categorizing TMZ with alkylating agents, antineoplastic agents, imidazoles, immunosuppressive agents, noxae, toxic actions and triazenes Table . FurtherTo evaluate the functional features of TMZ-mediated gene sets, we performed a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using WebGestalt. The top 15 KEGG pathways were identified based on genes associated with the three DNA repair systems Table . These pNext, to validate the pathways identified by WebGestalt, we additionally performed KEGG pathway enrichment using ClueGO. The glioma pathway was also screened for statistical significance in this analysis Figure . A broadp-value<0.001) as well as EGFR and TP53  exhibited mutual exclusivity  exhibited the most prominent changes, which were classified as missense mutations with a few cases of truncating mutations. For CDKN2A, alterations also included missense and truncating mutations. For EGFR, HRAS, KRAS and PTEN, few alterations existed in LGG cases  in the brain are slower growing than their high-grade counterparts. LGGs account for 10-20% of all primary brain tumors, and the median survival of LGG patients ranges from 4.7 to 9.8 years . Relateds Figure . Resultss Figure . Seven gp-value=0.00521) when patients were classified according to the mean value of the mRNA expression level  identified in the glioma pathway were used to conduct a survival analysis with clinical profiles in glioma. The results indicated that in LGG, a high level of PTEN expression predicted longer survival with statistical significance  were identified using STRING Figure . NotablyAs supporting evidence, the KEGG enrichment analysis conducted using WebGestalt and ClueGO identified the glioma pathway as significantly altered by TMZ-related genes Table . The assIn summary, the query of publicly available computational databases may significantly advance research by (1) unraveling the critical role of TMZ in glioma and revealing the mechanisms of glioma. Furthermore, by providing a deeper understanding of TMZ, the current analysis can assist in TMZ bio-curation, and new biological experimental designs will significantly accelerate glioma biology research. (2) This approach should facilitate early disease diagnosis and improve the accuracy of disease prognosis. The candidate genes identified by STRING, WebGestalt and the ClueGO database may facilitate the interpretation of genomic results and thus provide information useful for guiding research. However, several challenges remain for us to investigate and solve: (1) In addition to the glioma pathway, there are other cancer pathways identified by WebGestalt and ClueGO, such as bladder cancer and non-small-cell lung cancer. Therefore, whether the connectivity map shown in this paper to exist between TMZ and glioma can be extended to other solid tumors remains to be investigated. (2) The roles of drug targets detected by STRING must be further explored in the glioma pathway and in other signaling pathways under TMZ control. (3) Genomic alterations in the LGG samples differed from those in the GBM cases; these alterations may play a role in transition from low-grade glioma to high-grade glioma. Therefore, these genomic differences between LGG and GBM can be used to direct future research with reasonable experimental feasibility based on a functional network analysis. Overall, this paper provides a simple yet flexible procedure to test and validate reasonable hypotheses regarding genetic alterations in glioma by applying available biological information, such as in BioGPS, to assist researchers in translating basic studies to clinical applications.DrugBank is a web-based bioinformatic database containing comprehensive biochemical and pharma-cological information about drugs, their pharmacological mechanisms and their targets , 41. Thep-value<0.01 were selected. ClueGO is a cytoscape plug in app that visualizes non-redundant biological terms for large clusters of gene sets in a functionally grouped network [STRING v10.0 is an online database tool to analyze PPIs for differentially expressed genes and provide interaction information predicted by comparative genomics . In this network , 45. In The cBioPortal is an open-access resource for interactively exploring multidimensional cancer genomics datasets , 47. Onc


A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways. Finding and analyzing metabolic pathways that may span multiple organisms helps biologists to understand the metabolism, reconstruct metabolic network and discover candidate pathways for synthesis of useful biomolecules , 2. The The efforts on studying metabolic pathways can be divided into two complementary types , namely Graph-based pathfinding methods find possible metabolic pathways converting a given start compound to a given target compound through the connectivity of the reactions and the compounds in the metabolic networks. Some commonly used graph-based methods search metabolic pathways based on machine learning , 16, evo2O and H+  and brings the values of sim and fe(rij) into alignment.AGPathFinder uses the combined information of structural similarity between compounds and reaction thermodynamics to weight the edges, and AGPathFinder moves to the edges that are more thermodynamically favorable and/or link with more structurally similar nodes. Given an atomic group transfer graph α = 0.5, the values of weight Wij for reactions R00674 and R00685 are 0.469275 and 0.259005 respectively; when α = 1, the weight Wij only depends on the similarity between vi and vj, and the values of Wij for R00674 and R00685 are 0.625 and 0.2 respectively; when α = 0, the weight Wij only depends on rij, and the values of Wij for R00674 and R00685 become 0.31355 and 0.31801 respectively.In G and product H in reaction R and a user-specified size of atomic group, the following CAGM algorithm finds all conserved atomic groups of the user-specified size or larger transferred from G to H through reaction R [Algorithm 1].To construct an atomic group transfer graph from the start compound to the target compound, we need to compute the information for the atomic group transferred from substrate to product through reaction. Given substrate Algorithm 1: CAGMInput: substrate G in reaction R, product H in reaction R, conserved atomic group set Rg of G from start compound, edge mapping h for reaction R, user-specified size L of atomic group;Output: conserved atomic group set S of H; subgraph M of H;S←Φ;1. for each edge e∈E(Rg) where m1, m2∈V(Rg) do2. ifh(e) = e′ where e′∈E(H) and m1′, m2′∈V(H) then3.  V(M)←{m1′,m2′},where V(M)⊆V(H);4.   E(M)←, where E(M)⊆E(H);5.    end ifend forfor each unvisited node m in Mdo6. MC containing m in M by the depth-first search algorithm;7.  Find the connected component MC as visited;8.  Mark each node in if the number of nodes in MC≥ Lthen9.  S←S∪{MC}, where MC⊆M;10.    end ifend forReturnS.11. G is a start compound, then G is the only molecular in Rg. Let h be an edge mapping from G to H. At the beginning, CAGM finds all mapping edges from Rg to H by using h, and then uses these edges to construct subgraph M of H (lines 2–5). For each unvisited node m in M, the connected component MC containing m in M is determined by the depth-first search algorithm (lines 6–7), and each node in MC is then marked as visited (line 8). If the number of nodes in MC≥L, then MC is added to S (lines 9–10). Repeat this procedure until all nodes in M are visited.Initially, if In the following we use an example to explain the algorithm in finding the conserved atomic group transferred from substrate to product through reaction R02722 in G and compound C00078 is the product H. Let L = 2. The partition encircled with dotted line in C00065 is the conserved atomic group transferred from a start compound to C00065. This conserved atomic group constructs the conserved atomic group set Rg of C00065. At the beginning, we find all mappings of the edges {e1,e2} of Rg in C00078 by edge mapping h:{e1→f1, e2→f2, e3→f3, e4→f4, e5→f5}, and the resulting edge mappings are {e1→f1, e2→f2}. We then use f1 and f2 to construct a subgraph M of C00078. For each unvisited node m∈{O1t,C2t,O3t} in M, we find the connected component MC containing m in M by a depth-first search algorithm, and mark each node of MC as visited. From V(C00078) and the bond set {f1,f2}⊆E(C00078) form the MC. It is obvious that the number of nodes in MC≥2, thus MC is added to S, the algorithm terminates here since all nodes in M are visited.Example 2.1: Compound C00065 is the substrate Sm and target compound Tm, the following CAGTG algorithm creates a weighted atomic group transfer graph Gag between Sm and Tm, and finds the top k-shortest paths Cp with the smallest weight from Sm to Tm in Gag [Algorithm 2].The algorithm CAGM finds potential atomic groups transferred from substrates to products through reaction. Given start compound Algorithm 2: CAGTGInput: start compound Sm, target compound Tm, conserved atomic group set Rg from start compound, Boolean vector ψ, where Sc denotes compound similarity, Td denotes thermodynamic feasibility;Output: weighted atomic group transfer graph Gag between Sm and Tm, top k-shortest paths Cp with the smallest weight from Sm to Tm in Gag;Sm as visited;1. Mark Sm to Gag;2. Add Q←Sm;3. Queue While queue Q is not empty do4. vi←pop(Q);5.  Ifvi is not Tmthen6.  for each unvisited node vj adjoining to vido7.   S transferred from vi to vj by using algorithm CAGM;8.    Compute the conserved atomic group set vj as visited;9.    Mark IfS is not empty then10.    vi, vj) by Wij = α)+(1-α)(3200+fe(rij))/10000 with Boolean vector ψ;11.     Compute the weight of edge  to Gag;12.     Concatenate Q←{vj}∪Q;13.     Rg of vj with S.14.     Replace the conserved atomic group set    end if  end for end ifend whilek-shortest paths Cp with the smallest weight between Sm and Tm in Gag.15. Determine the top ReturnCp16. vi from queue Q (lines 4–5), where Q is the set of candidate nodes and these candidate nodes are used to create Gag. If node vi is not the target compound (line 6), for each unvisited node vj adjoining to vi, CAGTG executes algorithm CAGM to compute the conserved atomic groups transferred from vi to vj (lines 7–8), and mark node vj as visited (line 9). If S is not empty (line 10), CAGTG computes the weight of edge  by  (lines 10–11), add node vi and edge  to Gag (line 12), put node vj in Q (line 13), and replace the conserved atomic group set Rg of vj with S (line 14). CAGTG repeats this procedure until Q is empty. When Q is empty, the atomic group transfer graph between Sm and Tm has been created. Finally, CAGTG has found the top k-shortest paths Cp with smallest weight from Sm to Tm in Gag as candidate paths (lines 15–16).In an iterative manner, algorithm CAGTG removes node Sc and Td, which allow the user to manipulate the parameter α in ψ = ψ. If ψ = ψ, AGPathFinder uses α = 0 and it means that the weight of edge in ψ = ψ. If ψ = ψ, AGPathFinder uses α = 1and it means that the weight of edge in ψ = ψ. If ψ = ψ, AGPathFinder uses α = 0.5 and it means that the weight of edge in Our algorithm CAGTG provides two user-defined searching parameters The following example illustrates the process of creating weighted atomic group transfer graph between two compounds.Gag between start compound C1 and target compound C6. At the beginning of algorithm CAGTG, there is only one node in Gag. We put C1 in queue Q. In an iterative manner, we remove a node from Q each time. The first node removed from Q is C1, which is not the target compound. For the unvisited nodes C2, C3 and C7 adjoining to C1, we use algorithm CAGM to compute the atomic groups transferred from C1 to C2, C3 and C7 respectively, the resulting atomic groups are S2, S3 and S7. The atomic groups S2, S3 and S7 consist of sets of atoms {1,2,3}⊆V(C2), {2,3,4}⊆V(C3), and {2,3,4}⊆V(C7) respectively. The associated bond sets of S2, S3, S7 are {,}⊆E(C2), {,}⊆E(C3) and {,}⊆E(C7) respectively. We mark nodes C2, C3 and C7 as visited. Since the resulting atomic groups S2, S3 and S7 are not empty, we use Gag, and put C2, C3, C7 in queue Q. We replace the conserved atomic group sets of C2, C3 and C7 with S2, S3 and S7 respectively. Next, the node to be removed from Q is C2 which is (again) not the target compound. For the unvisited node C4 adjoining to C2, we use CAGM to compute the conserved atomic groups transferred from C2 to C4, the resulting atomic group is S4 with the atom set {2,3}⊆V(C4) and the bond set {}⊆E(C4). Node C4 is marked as visited. Since S4 is not empty, we compute the weight W3 of edge R3 by Gag, and put C4 in queue Q. We replace the conserved atomic group set of C4 with S4. This procedure is repeated until Q is empty. When Q is empty, the atomic group transfer graph between C1 and C6 has been created. Now the top k-shortest paths can be determined from Gag. For instance, if k = 2, we determine the top 2 shortest paths with the smallest weight from C1 to C6 in Gag as candidate metabolic pathways, and these 2 pathways are C1→R1→C2→R3→C4→R5→C6 and C1→R2→C3→R4→C5→R6→C6.Example 2.2: From the KEGG LIGAND database, we obtained 5848 compound structures and 7340 reactions which have corresponding KEGG RPAIR entries. We used the SMSD tool to compute the similarity between compounds. The atomic group transfer graph is built based on the KEGG RPAIR database. We have implemented AGPathFinder in Java.In order to evaluate the performance of AGPathFinder, the results are compared with several available metabolic pathfinding methods using atom tracking and an available graph-based method Tinker . These ahttp://osslab.lifesci.warwick.ac.uk/ tinker.aspx, http://www.kavrakilab.org/atommetanet and http://www.cs.helsinki.fi/group/sysfys/software/ReTrace respectively. AGPathFinder, LPAT and ReTrace were run on the Sugon 5000A parallel computer at Guangxi University, using a single computing node with a quad-core Intel(R) Xeon(R) CPU E5620 @ 2.40GHz and 40GB RAM. The running operating system is Linux. Tinker was implemented in C# and runs on a PC with Intel(R) Pentium(R) CPU G3240 @ 3.10GHz and 8GB RAM, and the running operating system is Windows 7. When Tinker was run to search pathways, the hub metabolites listed in [RouteSearch is a web-based pathfinding tool. We used RouteSearch to search pathways on Biocyc.org. We downloaded Tinker, LAPT and ReTrace from isted in   The compounds can be found in both computed and known pathways, which are called included compounds. (2) The order of the included compounds in the computed pathway is the same as the order of the included compounds in the known pathway.For each pathway, we use measures defined previously in the literature  to compuSn and PPV are defined as follows: Sn = TP/(TP+FN) and PPV = TP/(TP+FP), where true positives (TP) are the correct compounds found in the computed pathway, false negatives (FN) are the compounds in the known pathway but not in the computed pathway, and false positives (FP) are the compounds not in the known pathway but in the computed pathway [Ac = (Sn+PPV)/2 [Er for the compounds between computed pathway and known pathway. The smaller the error Er is, the more similar the computed pathway and the known pathway are. We can use the error Er to analyze the ability of pathfinding methods in recovering known pathways. Besides Ac, Sn, PPV and Er, we also use F-measure Fm for compound to evaluate the performance of pathfinding methods, where Fm = (2×PR×RC)/(PR+RC), PR is the precision and PR = PPV, RC is the Recall and RC = Sn, and Recall is the proportion of positive cases [Then the values of  pathway . Becausen+PPV)/2 . We use n+PPV)/2  to estimve cases .In addition to measuring the performance of the computed pathway based on compound, we also measure the performance of the computed pathways based on reaction. By analogy with the definition of correct compound, we derive the definition of the correct reaction in the computed pathways. The reactions in the computed pathway are called correct reactions if these reactions satisfy the following two conditions: (1) The reactions can be found in both computed and known pathways, which are called included reactions. (2) The order of the included reactions in the computed pathway is the same as the order of the included reactions in the known pathway.Sn_R and positive prediction value PPV_R for reaction are defined as follows: Sn_R = TP_R/(TP_R+FN_R) and PPV_R = TP_R/(TP_R+FP_R), where true positives (TP_R) are the correct reactions found in the computed pathway, false negatives (FN_R) are the reactions in the known pathway but not in the computed pathway, and false positives (FP_R) are the reactions not in the known pathway but in the computed pathway. The accuracy for reaction is Ac_R = (Sn_R+PPV_R)/2. By analogy with Er, we also use cross-validation [Er_R for the reactions between computed pathway and known pathway. Besides Ac_R, Sn_R, PPV_R and Er_R, we also use F-measure Fm_R for reaction to evaluate the performance of pathfinding methods, where Fm_R = (2×PR_R×RC_R)/(PR_R +RC_R), PR_R is the precision and PR_R = PPV_R, RC_R is the Recall and RC_R = Sn_R.The values of the sensitivity lidation  to estimIn this section, for each pair of start and target compounds of the 42 known pathways in the test, we use AGPathFinder, RouteSearch, Tinker, LPAT and ReTrace to find the top ten pathways. These top ten pathways are then compared to the known pathways and the results are shown in Tables Ac, Sn and Fm values of AGPathFinder in the case of ψ = ψ are a bit lower than those of RouteSearch, and the Er value of AGPathFinder in the case of ψ = ψ is a bit higher than that of RouteSearch.As can be seen from Tables Ac, PPV, Sn and Fm and lower values of Er than Tinker, LPAT and ReTrace whereas the performance of AGPathFinder is comparable with the performance of RouteSearch except for the sensitivity Sn. As can be seen from ψ = ψ and ψ = ψ, AGPathFinder performs better than Tinker, RouteSearch and ReTrace with the highest values of Ac_R, PPV_R, Sn_R, Fm_R and the lowest values of Er_R while in the case of ψ = ψ, the performance of AGPathFinder is comparable with the performance of LPAT. In the case of ψ = ψ, the Ac_R, PPV_R, Sn_R, and Fm_R values of AGPathFinder is a bit weaker than those of RouteSearch Retrace and LPAT, and the Ac, PPV, Sn, Fm and Er values of AGPathFinder is a bit weaker than those of RouteSearch.Regarding the performance of the best of top ten paths, in The results from Tables S-Paths and A-length of the pathways computed by each method where S-Paths is the number of the computed pathways and A-length is the average length of the computed pathways.It can be seen from Note that, for the computed pathways in The above results demonstrate that compared with RouteSearch, Tinker, LPAT and ReTrace, AGPathFinder can find shorter pathways with better biochemical performance in general.In the following, we investigate the impact of the size of atomic group, the compound similarity and the reaction thermodynamics on the performance of AGPathFinder. The performance of AGPathFinder under different parameter settings is shown in Tables ψ, the computed pathways that conserve the maximal size of atomic group have the highest values of Ac, Sn, PPV, Fm, Ac_R, PPV_R, Sn_R, and Fm_R. This confirms that the transfer of atomic groups from the start compound to the target compound is an important feature for metabolic pathways, and the size of an atomic group has direct impact on the biochemical performance of the pathways found by our method.Tables ψ directly influences the performance of AGPathFinder. For example, when we search pathways by tracking “Maximal size of atomic group”, for the top path, the setting ψ = ψ gives the highest values of Ac, Sn, PPV and Fm, and the setting ψ = ψ gives the highest values of Ac_R, PPV_R, Sn_R, and Fm_R. While the setting ψ = ψ produces the highest values of Ac, Sn, PPV, Fm, Ac_R, PPV_R, Sn_R, and Fm_R in the best of top ten paths.It can also be observed from Tables ψ = ψ, ψ = ψ and ψ = ψ respectively, and recovered 18, 21 and 22 known pathways that are returned as the top path in the cases of ψ = ψ, ψ = ψ and ψ = ψ respectively.Moreover, the use of combined information of compound similarity and reaction thermodynamics ensures that our method is still capable in finding meaningful pathways even without the option of tracking atomic groups. For example, without atomic group tracking and from a total of 42 known pathways in the test, AGPathFinder successfully recovered 28, 27 and 28 known pathways that are returned as the best of top ten paths in the cases of The above analysis clearly demonstrates that our method AGPathFinder improves the biochemical relevance of the computed pathways. In order to investigate the factors that may influence the biochemical relevance of the found pathways, we perform three representative test cases to analyze the results obtained by AGPathFinder, RouteSearch, Tinker, LPAT and ReTrace. The aim of this section is not to demonstrate the average or overall performance of pathfinding , but to gain insight into the characteristics of the methods through analysis.The biosynthesis of L-serine starts with 3-phospho-D-glycerate to L-serine. 3-phospho-D-glycerate contains 11 atoms, 3 of which are carbons, and L-serine contains 7 atoms, 3 of which are carbons. An atomic group containing 5 atoms  is transferred from 3-phospho-D-glycerate to L-serine in the biosynthesis of L-serine.r_c (the biosynthesis of L-serine in KEGG) and the pathways found by AGPathFinder, RouteSearch, Tinker, LPAT and ReTrace. The top ranking pathway returned by LPAT is r1 in KEGG. For all three settings ψ = ψ, ψ = ψ and ψ = ψ, all the top ranking pathways found by AGPathFinder are r_c in KEGG. The top ranking pathway returned by RouteSearch is r2 in the EcoCyc database [r3 in KEGG. The top ranking pathway returned by Tinker is r4 in the Rhea database [database . The topdatabase .ψ = ψ, the search of the pathways will be driven by Gibbs free energy of the reactions. To investigate the impacts of the utilization of energy on finding pathways from 3-phospho-D-glycerate to L-serine, ψ = ψ. These three pathways are represented by p1, p2 and p3 respectively.Recall that if r1, r2, r3 and r4, the pathway r_c for the biosynthesis of L-serine consists of reactions with minimal r_c in all cases with different settings of ψ. AGPathFinder is thus useful for retrieving known metabolic pathways. In addition, r1, r2, r3 and r4 are obviously different from r_c except for the start and target compounds. r2 is the shortest, but hub metabolite H+ occurs in its pathway.As we can observe from p3, the energies of the corresponding reactions in pathway p2 are lower and therefore p2 is ahead of p3. Note that our method is a shortest-path-based method, although the energies of the reactions in pathway p1 are not the lowest, p1 is the top ranking pathway since it is the shortest pathway. Moreover, the energies of the reactions in p2 are negative. This indicates that when we try to find the pathways releasing energy from 3-phospho-D-glycerate to L-serine, we can choose p2. On the other hand, the energies of the reactions in p1 and p3 can be negative or positive, and the user can find the pathways that either require or release energy such as p1 and p3.In Glycolysis starts from beta-D-Fructose 6-phosphate to phosphoenolpyruvate in aMAZE . Compounr_c (glycolysis in aMAZE) and the pathways found by AGPathFinder, RouteSearch, Tinker, LPAT and ReTrace. The top ranking pathway returned by LPAT is r1 in KEGG. The top ranking pathways returned by AGPathFinder are r2 and r3 in KEGG when ψ = ψ and ψ = ψ respectively. The top ranking pathway returned by AGPathFinder is r4 in KEGG when ψ = ψ. The top ranking pathway returned by RouteSearch is r5 in EcoCyc. The top ranking pathway returned by ReTrace is r6 in KEGG. The top ranking pathway returned by Tinker is r7 in Rhea.ψ = ψ. These three pathways are represented by p1, p2 and p3 respectively.To study the impacts of the utilization of energy on finding pathways from beta-D-Fructose 6-phosphate to phosphoenolpyruvate, r5 is similar to r_c, and their difference is that r5 does not bypass beta-D-Fructose 1,6-bisphosphate. Compared to r_c, the pathways r1, r2 and r3 do not go through beta-D-Fructose 1,6-bisphosphate and 3-Phospho-D-glyceroyl phosphate. In addition, r2 and r3 consist of an alternative pathway connecting beta-D-Fructose 6-phosphate with D-Glyceraldehyde 3-phosphate via reaction R01827. This alternative pathway is shorter than the pathway between beta-D-Fructose 6-phosphate and D-Glyceraldehyde 3-phosphate in r_c.As can be seen from r1, r2, r3 and r4, there is a shortcut linking D-Glyceraldehyde 3-phosphate and 3-Phospho-D-glycerate via R07159 and R01058 respectively, these shortcuts are annotated in the corresponding KEGG map00010 (Glycolysis/Gluconeogenesis). The difference between r4 and r_c is large except for the pathway from 3-Phospho-D-glycerate to Phosphoenolpyruvate. r4 goes through the pathway from beta-D-Fructose 6-phosphate to D-Glyceraldehyde 3-phosphate as a result of the high similarities between the compounds contained in this part of r4, for example, both similarity between beta-D-Fructose 6-phosphate and Sorbitol 6-phosphate and similarity between D-Glucose 6-phosphate and D-Fructose 6-phosphate are 1. For the same reason, r4 bypasses the pathway from D-Glyceraldehyde 3-phosphate to 3-Phospho-D-glycerate. This indicates that one can use compound similarity to filter pathway assignments for feasibility.Furthermore, in r6 and r7 is common with those of r_c, except for the start and target compounds. We can also see that, r2 and r3 are very similar to r1, and they only differ in one reaction. These results show that AGPathFinder and LPAT are capable in finding similar alternative pathways of glycolysis.In addition, none of the reactions and compounds in p1 is the shortest pathway and therefore it is the top ranking pathway. Although the length of pathways p2 and p3 is the same, p2 is ahead of p3 since the sum of the energies of the reactions in p2 is lower. In p1 and p2, the energies of the reactions are negative, which demonstrates that we can search for the pathways releasing energy such as p1 and p2 from beta-D-Fructose 6-phosphate to phosphoenolpyruvate. In p3, the energy of reaction R01070 is positive whereas the energies of other reactions are negative, thus we can find the pathway that either require or release energy like p3.Seen from S. cerevisiae and the bacteria E. coli, respectively. An atomic group with 7 atoms  is transferred from L-Aspartate to L-Methionine in the biosynthesis of L-Methionine.The biosynthesis of L-Methionine starts with L-Aspartate to L-Methionine. L-Aspartate contains 9 atoms, 4 of which are carbons, and L-Methionine contains 9 atoms, 5 of which are carbons. Two variants of this pathway are characterized in the yeast r_ce (the biosynthesis of L-Methionine for bacteria E. coli in aMAZE) and r_cs (the L-Methionine biosynthesis pathway for the yeast S. cerevisiae in aMAZE), and the pathways found by AGPathFinder, RouteSearch, Tinker, LPAT and ReTrace. The top ranking pathway returned by RouteSearch is r_ce in EcoCyc for E. coli K-12 substr. MG1655. The top ranking pathway returned by LPAT is r1 in KEGG. For all three settings of ψ, the top ranking pathway returned by AGPathFinder is r2 in KEGG. Note that r2 is the only pathway found by AGPathFinder in the case of ψ = ψ. The top ranking pathway returned by RouteSearch is r2 in EcoCyc for S. cerevisiae. The second-ranked pathway returned by AGPathFinder is r3 in KEGG with the setting ψ = ψ. The top ranking pathway returned by ReTrace is r4 in KEGG. The top ranking pathway returned by Tinker is r5 in Rhea.r2 corresponds closely to r_cs, and their difference is that r2 does not go through L-Homocysteine and reaction R04405. Because r2 via reaction R00651 is shorter, AGPathFinder chooses reaction R00651 in the process of inferring r2. In r2, the energies of the reactions can be negative or positive, and we can find the pathway that either require or release energy like r2 in the case of ψ = ψ.From r3 is very similar to r_cs except for one reaction. The value of r3 is lower than the value of r3. Furthermore, r1 is similar to r_cs, but their difference is far greater than the difference between r3 and r_cs. In addition, r4 and r5 are completely different from r_ce and r_cs except for the start and target compounds. These alternative pathways demonstrate how AGPathFinder and other four methods can be used to expand the metabolism of L-Methionine synthesis. Through efficient search in the extensive spaces in designing synthetic metabolic pathways, the computational pathfinding methods can find new pathways producing the same target compound through different mechanisms than those already known. These pathways need to be further tested for biological and biochemical consistency before implementation. However, the results show promising alternatives to generate valuable products.Alternative pathway This article presents a pathfinding method AGPathFinder for finding metabolic pathways. The main feature of AGPathFinder is its integration of atomic group tracking and combined information of reaction thermodynamics and compound similarity into the search of metabolic pathways. This feature distinguishes AGPathFinder from existing atom tracking pathfinding methods, which are restricted to track the user-defined atoms in the search for alternative pathways.In section “Results”, in most cases, we have shown that the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of RouteSearch, Tinker, LPAT and ReTrace. Atomic group tracking, when combined with weighted metabolite graph, improves the quality of the found pathways. With the introduction of atomic group tracking, our method does not require the user to define the atoms to be tracked neither to exclude hub metabolites in advance. On the other hand, the use of combined information of reaction thermodynamics and compound similarity ensures that our method is still capable in finding meaningful pathways even without the option of tracking atomic groups. The results have demonstrated that AGPathFinder successfully recovers the known pathways and finds the thermodynamically feasible pathways that avoid spurious connections. Moreover, AGPathFinder allows the user to define biochemical parameters to search for specific pathways of interest, and it returns pathways with the information of reaction thermodynamics and compound similarity.in vivo experimentation.AGPathFinder infers pathways across all of the data in KEGG, but for some applications, researchers may be only interested in the metabolic network of a single organism or several related organisms. A possible solution for this issue is to use alternative weighting schemes. For example, we try to find organism-specific pathways by weighting the reactions depending on the possibility that the organism of interest performs the corresponding reactions. This may help to find feasible candidates for In the current work, we have not considered the substrate availability or toxicity, and did not take the different reaction conditions such as pH and temperature into account when estimating thermodynamics. In future work, we intend to include these factors into AGPathFinder. Another interesting extension is to combine constraint programming methods with atomic group tracking in searching metabolic pathway.http://210.36.16.170:8080/AGPathFinder/data.zip.AGPathFinder is fully implemented in Java. The data and the program can be downloaded from http://210.36.16.170:8080/AGPathFinderWeb/login.jsp.AGPathFinder is also available as a web service at S1 Text(PDF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.


The analysis allows the identification of critical elements that have to be activated to provoke proliferation.In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on The technological advances in the analysis of global gene expression and the growth of genomic sequence information have revolutionized research in biology and biomedicine , 2. InveThe goal of our research is to develop abstract qualitative models of metabolic and signaling processes that can be used as the basis of further analyses by powerful tools . We model and analyze the biological processes by which an initial cellular system can lead to the activation of proliferation signaling by using a ligand/receptor pathway. The cells receive external signals by certain biomolecules (ligands) that are able to interact with certain receptors on the cellular surface producing some effects inside the cell . A ligan narrowing. Moreover, we present an application of classical forward search with EGF signaling pathway. Authors have published other forward search applications in ) and binds to protein EGF (Egf) ) and the cytoplasm contains the tyrosine phosphatase Shp2, then Shp2 is phosphorylated on tyrosine and recruited to the EgfRC container [ontainer . This siWhen protein EGF (Egf) is bound to its receptor EGFR phosphorylated on tyrosine ([EgfR - Yphos]), the GRB2-associated-binding protein Gab1 or Gab2 phosphorylated on tyrosine is present ([gab:GabS - Yphos]), the Ras guanyl-releasing protein 3 RasGrp3 or protein Sos1 phosphorylated on tyrosine is present, Pi3k is present, the protein Shp2 phosphorylated on tyrosine is present, and the inside of the membrane (Cli) contains Hras loaded with guanosine diphosphate GDP, then Hras switches its load to guanosine 5′-triphosphate GTP : Egf}.We can use the rule {XOut ∣ xout}  {EgfRC ∣ egfrc}. This way, we do not impose any constraint to the initial term. The narrowing process would find that the variable xout must be of the form xout' Egf, while egfrc must be of the form egfrc' EgfR, for xout' and egfrc' fresh variables. This way, we would obtain the term {XOut ∣ xout'} {EgfRC ∣ egfrc' ([EgfR - Yphos]: Egf)}, which is more general than the one above. Since the narrowing process returns the bindings applied to the initial variables, we can use it to check whether a state is reachable from a general term and then obtain the most general initial term required to reach this state, hence allowing us to discard the infinite initial dishes that are a particularization of this solution.Note that starting with a ground term greatly limits the power of the search, since (i) a slight variation in this term might imply huge differences in the reachable terms and (ii) it limits the causes of the reachable states to that dish, hence ignoring other simpler or more general causes. In order to solve this problem, narrowing is applied to terms with variables that stand for potentially infinite sets of ground terms. In that case, we could start the process with the term https://github.com/ariesco/pathway.However, due to its computational power, narrowing poses a number of important restrictions. First, it can only be applied to unconditional theories. Secondly, since unification is only available for some particular theories, only some combinations of equational axioms are allowed. Finally, it is required for all the rewrite rules to be defined at the top (the rules must be applied to the complete term and not just to the subterms). Luckily enough, the Pathway Logic specification fulfills the first two constraints, although it fails to fulfill the last one. For this reason, we have developed a way to apply narrowing to any pathway. It is worth discussing the entity and complexity of this extension, whose main points are as follows: (i) it is a metalevel function, which is able to modify the rules in a given module to make them fulfill the constraints above, and (ii) it is Full Maude , since nFor example, we look for the different dishes that can produce the first result for the search in Although the first solution just indicates that we can reach this result by applying no rewrite rules and starting with the same substitution we are looking for, the second solution goes one step backwards and proposes the initial state shown in By looking for further solutions, Maude keeps going backwards until the initial dish is as general as needed by the user. Also note that the initial dish can be further specialized to indicate that we expect some initial components or to focus in a particular element. We consider that this technique, which has been proposed for the first time in this paper, will be very useful for analyzing the causes of many pathways.In the last few years, the analysis of biological systems has required an overwhelming and boundless amount of quantitative data with the use of new technologies –63. ObtaThe dynamics of complex biological systems can be explained by developing symbolic methods . MoleculRewriting logic provides a logical framework that gives us the ability to build and analyze models with multiple levels of detail . DiffereIn this paper, we have described two different searches for studying the epidermal growth factor in the Maude implementation of the Pathway Logic. The first one allows the user to study how different particular dishes can evolve by applying a breadth-first search by rewriting, hence being able to check whether a certain state is reachable. The second search presents a novel approach by applying narrowing instead of rewriting, which allows the user to trace back the possible origins of a given scenario, hence providing a causality study. In order to perform this analysis, we have extended Full Maude, the tool where the narrowing capabilities of Maude are implemented, with a command that preprocesses the Pathway Logic modules in order to fulfill the narrowing requirements. We expect that this novel approach will be useful for analyzing other pathways where the forward approach has already been used.


Metabolic pathway analysis is a popular strategy for comprehensively researching metabolites and genes of interest associated with specific diseases. However, the traditional pathway identification methods do not accurately consider the combined effect of these interesting molecules and neglects expression correlations or topological features embedded in the pathways. In this study, we propose a powerful method, Subpathway-CorSP, for identifying metabolic subpathway regions. This method improved on original pathway identification methods by using a subpathway identification strategy and emphasizing expression correlations between metabolites and genes of interest based on topological features within the metabolic pathways. We analyzed a prostate cancer data set and its metastatic sub-group data set with detailed comparison of Subpathway-CorSP with four traditional pathway identification methods. Subpathway-CorSP was able to identify multiple subpathway regions whose entire corresponding pathways were not detected by traditional pathway identification methods. Further evidences indicated that Subpathway-CorSP provided a robust and efficient way of reliably recalling cancer-related subpathways and locating novel subpathways by the combined effect of metabolites and genes. This was a novel subpathway strategy based on systematically considering expression correlations and topological features between metabolites and genes of interest within given pathways. We analyzed lenient distance similarities of important nodes within metabolic pathways to locate key subpathway regionsIn this study, we proposed a powerful method called Subpathway-CorSP to identify metabolic subpathway regions by integrating expression correlations and topological features of metabolites and genes of interest within pathways. Importantly, we improved on original pathway identification methods by using subpathway identification strategy and emphasizing expression correlations between metabolites and genes of interest based on topological features within the metabolic pathways. First, we integrated differentially expressed (DE) metabolites, DE genes, as well as metabolites and genes with high expression correlation related to the study condition (prostate cancer) into the corresponding compound and enzyme nodes (nodes of interest) within reconstructed metabolic pathway graphs. Then, we considered expression correlations between metabolites and genes of interest based on topological features to locate key metabolic subpathway regions whose internal structures were highly correlated for expression. Finally, we used hypergeometric test to evaluate the enrichment significance of these subpathway regions. Subpathway-CorSP was applied to a prostate cancer data set and its metastatic sub-group data set. We demonstrated that the method was able to identify multiple disease-related subpathway regions robustly and efficiently.We applied Subpathway-CorSP to the identification of prostate cancer-related metabolic subpathways. The schematic overview of Subpathway-CorSP is in 48978We identified 48 DE metabolites by Wilcoxon rank-sum test (P < 0.05) and 1182 DE genes by SAM method (FDR < 0.001) between benign prostate samples (Benign) and prostate cancer samples (PCA+ Mets). Meanwhile, we identified 40 metabolites and 5581 genes with high expression correlation using Pearson correlation coefficient (Pcc) >0.8. We obtained 63 metabolites and 5846 genes of interest by combining a union set of the above metabolites and genes . With thWith a statistical significance level of 0.05, Subpathway-CorSP identified 20 significant metabolic subpathways. These subpathways also corresponded to 20 entire metabolic pathways . Of thesSubpathway-CorSP tended to locate key subpathway regions that mainly considered expression correlation information. Therefore, we tested if the significant subpathways identified showed clearly higher expression correlations. We obtained all Pcc values of metabolite/gene pairs within 20 significant subpathways and calculated those within any single metabolic pathway. The average Pcc value for the 20 significant subpathways was significantly higher than for the corresponding 20 entire pathways  . The aveWe noticed that up to eight pathways identified by Subpathway-CorSP were simultaneously not detected by Pathway-G, Pathway-M, IMPaLA and Subpathway-GM . These pin vitro and reduce prostate tumor growthThe most significant subpathway (path:00561_1) of the three additional subpathways was in glycerolipid metabolism pathway . SubpathThe second significant subpathway (path: 00270_2) belonged to a cysteine and methionine metabolism pathway and had a P-value of 0.0153 . The corThe third significant subpathway path:00360_1) belonged to a phenylalanine metabolism pathway . Subpath0_1 belonTo validate the stability and reliability of the significant metabolic subpathways, stability analysis was performed. We extracted part of original samples as new data sets, and then identified new significant metabolic subpathways. We tested the number of original significant subpathways that were recalled. The more recalled subpathways, the more stable the subpathways that were identified. The steps were: First, keeping the metabolites and genes invariant, we simultaneously chose 75% of the benign prostate samples (Benign) and 75% of the prostate cancer samples (PCA+ Mets) without replacement from both the metabolomic profile and the matched gene expression profile. This yielded a training data set containing 12 benign prostate samples and 18 prostate cancer samples. Second, with the training data set, we performed Subpathway-CorSP strategy again and identified new significant metabolic subpathways. Third, the recalled number of original significant subpathways was counted, and the training experiment repeated 80 times. At least 12 and at most 18 significant subpathways were recalled . The recThis study focused on identifying key metabolic subpathways by integrating expression correlations and topological features between nodes of interest within pathways. When two nodes of interest had higher Pcc value and smaller shortest path, they had larger CorSP score, which represented their close connection in a pathway. Thus, these two nodes tended to be added to one node set (that was a subpathway), and other nodes at their shortest path were added to the same node set simultaneously. Based on this theory, Subpathway-CorSP identified key subpathway regions in entire metabolic pathways. To set an appropriate threshold, m, for the identification of effective subpathways, we computed CorSP scores for each two nodes of interest in all metabolic pathways. We set m, by experience, to represent the score for 70% CorSP scores passing threshold m for all 150 metabolic pathways . The thrTo evaluate the specific pathological features of metastatic prostate cancer and further demonstrate the use of Subpathway-CorSP, we identified metastatic prostate cancer-related metabolic subpathways. Considering different types of samples that were mixed into a single dataset, we used 12 localized prostate cancer samples (PCA) and 12 metastatic prostate cancer samples (Mets) to identify metabolites and genes of interest. Thus, 74 metabolites and 8765 genes of interest associated with metastatic prostate cancer were obtained . SubpathSpecial metabolic pathways only significantly associated with metastatic prostate cancer included purine metabolism (path:00230), the beta-alanine metabolism pathway path:00410), pyrimidine metabolism path:00240), the glycosphingolipid biosynthesis-globo series (path:00603) and the riboflavin metabolism pathway (path:00740). In these significant subpathways, nodes of interest showed denser expression correlations than the entire corresponding pathways. To the best of our knowledge, these pathways are not directly related to metastatic prostate cancer. However, the significant subpathway (path:00410_2) identified by Subpathway-CorSP captured a key region of beta-alanine and uracil conversion in the beta-alanine metabolism pathway . Beta-al, the gly0, pyrimiMetabolic pathways are important for interpreting metabolic functions of pathways related to specific diseases1617et al. proposed an extension of the linear combination test, which is used for testing correlations between pathways and correlated multiple continuous phenotypes based on gene expressionet al. illustrated that separate analyses of up- and down- regulated genes identify more pathwaysResearch has been devoted to pathway analysis based on the idea of correlation. For example, EnrichNet performed a random walk with restart to identify crosstalk between known gene sets and pathway genes under the background of gene interaction networksWe developed a novel subpathway identification strategy called Subpathway-CorSP that integrated metabolites and genes of interest related to a given cancer into metabolic pathways. We identified key metabolic subpathway regions by systematically considering expression correlations and topological features between metabolites and genes of interest within the pathways. Especially, Subpathway-CorSP was an improvement on Subpathway-GM by emphasizing expression correlations between nodes of pathways. For the input data, Subpathway-GM treated DE genes and metabolites as interesting gene and metabolite sets. However, in Subpathway-CorSP, we integrated DE metabolites, DE genes, as well as metabolites and genes with high expression correlation related to the study condition as metabolites and genes of interest. Although the two methods were both based on expression profiles, Subpathway-CorSP further considered expression correlation information of metabolites and genes, defining metabolites and genes with high Pcc value as important. As a subpathway identification method, Subpathway-GM used a lenient distance similarity of signature nodes to locate subpathways. If the shortest path length between two signature nodes was shorter than n+1, then the two signature nodes and other non-signature nodes were added to the same node set. Thus, from a network-structure point of view, Subpathway-GM fully considered topological correlation between nodes within pathways. However, Subpathway-GM did not account for how metabolites and genes within the pathways correlated at the level of expression. Subpathway-CorSP further considered expression correlations of metabolites and genes based on topological features. When locating subpathways, we continued to apply lenient distance similarity and further improved it using CorSP score, instead of just the shortest path length between two nodes of interest. Specifically, for each pathway that contained nodes of interest, we computed Pcc and the shortest path between each two nodes of interest, and then computed CorSP score. When CorSP score between the two nodes of interest was greater than a given threshold, the nodes and other nodes at their shortest path were added to the same node set.In this study, Subpathway-CorSP was mainly applied to prostate cancer data set. Many pathways corresponding to significant subpathways identified by Subpathway-CorSP were not detected by the traditional pathway identification methods Pathway-G, Pathway-M, IMPaLA and Subpathway-GM. The different results were mainly attributed to two points. One was the difference in use of the data set. Pathway-G used only DE genes, Pathway-M used only DE metabolites. The other difference was that Subpathway-CorSP considered not only pathway topology, but also expression correlations between metabolites and genes of interest. Both IMPaLA and Subpathway-GM did not use expression correlation information. Our analysis found that the average Pcc value for the 20 significant subpathways was significantly higher than for all the metabolic pathways. This result showed that significant subpathways identified by Subpathway-CorSP had clearly higher expression correlations. These results demonstrated that Subpathway-CorSP improved the identification of subpathways by considering expression correlations.http://222.170.78.233/Subpathway-CorSP/.We focused on three of eight pathways identified by Subpathway-CorSP but not the other methods. We found they were highly associated with prostate cancer, suggesting that Subpathway-CorSP was able to identify more prostate cancer-related metabolic pathways. The stability analysis demonstrated stability and reliability of these significant metabolic subpathways. Flexibility could be introduced to this subpathway strategy by varying threshold m, which could be adjusted by users. As threshold m decreased, the number of the other nodes except for nodes of interest within subpathways increased, and thus the scale of the subpathways increased. Application of Subpathway-CorSP to a metastatic sub-group data set showed that Subpathway-CorSP located potential metastatic prostate cancer-related metabolic subpathways. The codes for Subpathway-CorSP can be downloaded at We reported the implementation of a novel subpathway identification strategy to investigate the combined effect of metabolites and genes by integration of expression correlations and topological features within the metabolic pathways. The method had some limitations. Although KEGG is a popular and widely used pathway database resource, some true interactions might be missed when KEGG is used as the single data sourceet al.http://www.metaboanalyst.ca) is a comprehensive tool suite for metabolomic data analysis to convert metabolite names to KEGG compound IDshttp://www.metaboanalyst.ca/ faces/Secure/utils/ConvertView.xhtml). We mapped the 518 metabolites to 100 metabolites with KEGG compound IDs. Matched gene expression profile data  was downloaded from the GEO databaseUnbiased metabolomic profile including 16 benign prostate samples adjacent to tumor (Benign), 12 localized prostate cancer samples (PCA) and 12 metastatic prostate cancer samples (Mets) was from Sreekumar Subpathway-CorSP identified metabolic subpathway regions by integrating expression correlations and topological features between metabolites and genes of interest. Metabolic subpathways were identified mainly through: (i) mapping metabolites and genes of interest to graphs of metabolic pathways after graph-based reconstruction of metabolic pathways; (ii) developing subpathway identification algorithm and locating metabolic subpathways within pathways according to nodes of interest; (iii) evaluating the statistical significance of metabolic subpathway regions. The detailed steps are described below.http://www.genome.jp/kegg/xml/) of KEGG pathways and converted them to list variables in R by applying iSubpathwayMiner, an R package developed by our research groupWe constructed a bipartite network to represent metabolic pathways in the KEGG pathway database50Based on gene expression data and metabolomic experimental data, we respectively identified DE metabolites and DE genes using Wilcoxon rank-sum test and SAM method, and calculated Pcc for each metabolite-metabolite, metabolite-gene and gene-gene pair. We extracted metabolites and genes with Pcc greater than a cutoff such as 0.8, and regarded them as metabolites and genes with high expression correlation. We defined DE metabolites, DE genes, as well as metabolites and genes with high expression correlation as metabolites and genes of interest. Metabolites and genes of interest were mapped to corresponding nodes within pathway graphs. Notably, metabolites of interest were mapped to corresponding compound (substrate and product) nodes and genes of interest were assigned to KO identifiers and matched to enzyme nodes. Mapped nodes within each pathway graph were defined as nodes of interest.ij score was computed to evaluate the expressional and topological influence between the ith and jth nodes of interest. CorSPij was defined as:We developed a novel subpathway identification strategy to locate metabolic subpathway regions by systematically considering expression correlations and topological features between nodes of interest within given pathways. Specifically, for each pathway that contained nodes of interest, we computed Pcc and the shortest path between each two nodes of interest. A CorSPij is Pcc between node i and node j based on their expression profiles, dij is the shortest path between node i and node j in any one pathway, calculated by breadth-first search algorithm, and β is a parameter set to balance the relationship between Pcc and the shortest path. In this study, we set β = 10.where, cij between the ith and jth nodes of interest was greater than threshold m, the nodes and other nodes at their shortest path were added to the same node set. This process was recurrently computed for all nodes of interest. We extracted corresponding subgraphs in pathway graphs according to each node set, and defined these subgraphs with node number ≥s as subpathway regions because subgraphs with small scales could not usually form biologically significative subpathways.We located subpathway regions according to CorSP scores between nodes of interest. There are many feasible distance measures in the area of computer science52To evaluate the statistical significance of the located subpathways, hypergeometric tests were used, which required the following values: (i) number of metabolites and genes of interest submitted for analysis; (ii) number of background metabolites and genes; (iii) number of metabolites and genes of interest annotated to each metabolic subpathway; and (iv) number of background metabolites and genes annotated to each metabolic subpathway. All metabolites in the Human Metabolome Database (HMDB)m (mg) was number of metabolites (genes) in the entire metabolome (genome), and nm (ng) was number of metabolites (genes) of interest, of which rm (rg) metabolites (genes) involved in the subpathway containing tm metabolites (tg genes).where, mHow to cite this article: Feng, C. et al. Subpathway-CorSP: Identification of metabolic subpathways via integrating expression correlations and topological features between metabolites and genes of interest within pathways. Sci. Rep.6, 33262; doi: 10.1038/srep33262 (2016).


Adult male Muta™Mouse were gavaged for 28 days with seven individual, genotoxic PAHs. Global gene expression profiles in forestomach, liver, and lung (target tissues of exposure) were determined at 3 days post-exposure. The results are compared with our previously published results from mice exposed to BaP via the same exposure regimen. Although all PAHs showed enhanced ethoxyresorufin-O-deethylase activity, DNA adduct formation, and lacZ mutant frequency in the lungs, the unsupervised cluster analysis of differentially expressed genes revealed that the transcriptional changes are both PAH- and tissue-specific, with lung showing the most response. Further bioinformatics-/pathway-based analysis revealed that all PAHs induce expression of genes associated with carcinogenic processes, including DNA damage response, immune/inflammatory response, or cell signaling processes; however, the type of pathways and the magnitude of change varied for each PAH and were not the same as those observed for BaP. Benchmark dose modeling showed transcriptomic data closely reflected the known tumor incidence for the individual PAHs in each tissue. Collectively, the results suggest that the underlying mechanisms of PAH-induced toxicity leading to tumorigenesis are tissue-specific and not the same for all PAHs; based on the tissue type considered, use of BaP as a reference chemical may overestimate or underestimate the carcinogenic potential of PAHs.In this study, the accuracy of the assumption that genotoxic, carcinogenic polycyclic aromatic hydrocarbons (PAHs) act via similar mechanisms of action as benzo(The online version of this article (doi:10.1007/s00204-015-1595-5) contains supplementary material, which is available to authorized users. For exaHuman health risk assessment of PAH-contaminated complex environmental mixtures considers the incremental carcinogenic risk of a targeted set of PAHs as toxicological equivalents of BaP (relative potency factor approach). However, to be able to apply this approach, all structurally similar PAHs in a mixture must elicit similar toxicological responses via similar modes/mechanisms of action. The most well-established mode of action of genotoxic, carcinogenic PAHs involves binding and activating the aryl hydrocarbon receptor (AhR), the expression of genes associated with xenobiotic metabolism, and the concurrent metabolism of PAHs. This in turn leads to the production of reactive intermediates  that covalently bind DNA and form bulky DNA adducts. When left unrepaired, these DNA adducts can result in mutations in tumor-suppressor genes or oncogenes, which can eventually lead to carcinogenesis, implying a genotoxic mode of action in cancer. Consequently, measurement of DNA adducts and DNA mutations have long been taken as measurable markers of the genotoxic mode of action of PAHs. However, the existing toxicological database (showing evidence for carcinogenicity and the details pertinent to the underlying mechanisms of carcinogenicity) for the individual PAHs is incomplete and thus lacks experimental support for the use of the reference chemical method  assay.In the present study, we sought to explore the underlying carcinogenic modes of action of several individual PAHs to specifically investigate whether the assumption of similar mode of action is accurate. We exposed Muta™Mouse males to three doses of seven PAH congeners or vehicle for 28 consecutive days via oral gavage. These seven PAHs are known to be genotoxic and constitute the panel of PAHs recommended for routine measurement in contaminated soils for the protection of human and environmental health in the Canadian soil quality guidelines CCME  and in ca)anthracene (BaA), benzo(b)fluoranthene (BbF), benzoperylene (BghiP), benzo(k)fluoranthene (BkF), dibenzanthracene (DBahA), indenopyrene (IP) , and chrysene (Chr) . All of these PAHs are classified as possible or probable human carcinogens by IARC and the USEPA  and RNeasy Mini Kits following the protocols previously published  under the accession number GSE51321. Differentially expressed genes (DEGs) were defined as genes with FDR-adjusted P values less than 0.05 (FDR P ≤ 0.05) from the pairwise analysis to controls with absolute fold change estimates greater than or equal to 1.5 in either direction (fold change ±1.5). Hierarchical cluster analysis was applied to the filtered data and visualized using a heatmap using R  following previously published protocols  from each PAH were independently analyzed to identify biological functions or processes perturbed in response to the treatment. DAVID  pathway analysis and network process analysis were used for functional classification. The canonical pathway analysis, biological function analysis, and network analysis within Ingenuity Pathway Analysis  were used to identify biological pathways and functions associated with the DEGs. Since there was a large redundancy in the annotation of the perturbed biological processes, pathways, and functions, the results were further grouped under parent pathways/processes (Online Resource 2). IPA’s Upstream Regulator analysis was performed to determine the regulatory mechanisms (transcription factors and receptors) operating in response to individual PAHs, and these were compared to BaP. Online Resource 1D describes the criteria used to determine the statistical significance of altered pathways, functions, biological processes, or upstream regulators.The lists of DEGs (FDR http://nextbio.com) was used to compare the transcriptomic responses following exposure to individual PAHs and following exposure to BaP. A less stringent non-FDR P value cutoff of less than 0.05 and a fold change of ±1.2 was applied. A pairwise comparison was made for each gene between the two datasets . A rank-based enrichment statistic was applied to determine the final correlation score. If the direction of change in the expression of a gene following exposure to a congener PAH was the same as the direction of change in expression of that gene following exposure to BaP, then the correlation was positive.The NextBio meta-analysis function , two emerging hallmarks , and two enabling characteristics , as described in Hanahan and Weinberg . Re-clasFrozen lung tissues were sliced randomly for isolation of genomic DNA by phenol/chloroform extraction as described previously -positive selection assay was conducted as described previously  assay. Since the available amount of tissue from each treatment group was small, samples from the same treatment group were pooled (Online Resource 1H).Enzymatic analysis of pulmonary CYP1A activity was assessed via the ethoxyresorufin-lacZ mutant frequency) in lungs. Further, BMDs were modeled for the total number of DEGs, the number of DEGs related to the hallmarks of cancer, and for the number of hallmarks of cancer perturbed by each PAH in the forestomach, liver, and lung tissues using the USEPA’s Benchmark Dose software BMDS version 2.5.0 (http://www.epa.gov/ncea/bmds/) . Continuous data were run against five models , and proportional (dichotomous) data were run against eight models . The benchmark response (BMR) was set to 10 % extra risk as recommended by the Benchmark Dose Technical Guidance document (USEPA P < 0.1.BMD and BMDL (lower confidence limit of BMD) values were modeled for apical endpoint measurements  for 28 consecutive days via oral gavage. The exposure regimen did not cause any overt signs of toxicity, and there was no significant body weight loss in any of the exposed mice compared to vehicle-treated controls. Forestomach, liver, and lung tissues were collected 3 days after the final exposure.The transcriptomic profiles derived from the present study were compared with the profiles of mice exposed to BaP (following the same exposure regimen described for other seven PAHs in this study) to investigate whether BaP, as a reference PAH, accurately represents the toxicity posed by other individual PAHs. Global transcriptomic changes in the forestomach, liver, and lung tissue were characterized.P ≤ 0.05, fold change ±1.5) for each tissue and the different doses tested. Online Resource 3 provides the full lists of DEGs including the gene names, gene accession numbers, P values, and fold changes for the forestomach, liver, and lung. The results are presented by tissue type below.Table Responses to each individual PAH were analyzed separately, and the results were then compared across all PAHs. Of all the PAHs tested, BaP induced the largest response in forestomach. Hierarchical cluster analysis was conducted using 1008 DEGs that showed differential expression in at least one dose group for at least one PAH  compared to the FDR-adjusted P value approach and used a ranking of genes based on their fold changes to explore the correlation of the trends in gene ranking. The results revealed a strong correlation between BaP, BbF, and BaA, whereas BghiP and IP were least correlated with BaP. Chr was negatively correlated with BaP.In addition to comparison of DEGs by VENN analysis, a meta-analysis of all datasets was conducted using NextBio. Since most of the PAHs induced very subtle responses at the low or medium doses, the meta-analysis was conducted on the high-dose groups of each PAH Fig. c. This aP ≤ 0.05), KEGG pathways (EASE P ≤ 0.05), IPA canonical pathways (P ≤ 0.05), and MetaCore process networks (P ≤ 0.05). Since there was a large redundancy in the annotation of the perturbed biological processes, pathways, and functions, the results were further grouped under parent pathways/processes (Online Resource 1).Figure Five main functional categories of pathways and processes were perturbed in forestomach following exposure to BaP: DNA damage response, altered cell signaling, immune and inflammatory response, altered metabolism, and cytoskeletal organization Fig. d. SpecifTo focus on the cancer mode of action, pathway perturbations that functioned in processes related to the published hallmarks of cancer were examined in detail . Other BaP-perturbed pathways such as ubiquitination, antigen processing and presentation (APP), pattern recognition receptors, xenobiotic metabolism, and epithelial cell differentiation were altered following exposure to BaA, BbF, BbF, or BkF, respectively. Chr, DBahA, and IP did not have any pathways in common with BaP. We note that although endocytosis was altered by both BaP and BkF treatment, the direction of change was different; endocytosis appeared to be upregulated following BaP exposure and downregulated following BkF exposure, suggesting that the final toxic outcome of this change may be different for these PAHs  responsible for regulating the expression of DEGs (Online Resource 4) in the forestomach for each of the PAHs. Very few commonalities were observed between BaP and the other PAHs. The results suggest that the transcriptomic changes induced in forestomach by each of the PAHs are mediated by different regulatory molecules.Figure Of the PAHs tested, BbF, BaA, and DBahA induced the most changes in hepatic gene expression. Hierarchical cluster analysis was conducted using 977 DEGs that were differentially expressed in at least one dose group for at least one PAH. Cluster analysis revealed that each PAH clustered separately, suggesting PAH-specific transcriptomic responses in the liver Fig. a. ExceptA meta-analysis of all DEGs from the high-dose group of all the PAHs was conducted in NextBio Fig. c. TranscBaP-induced hepatic transcriptomic responses were functionally separated into four main categories of pathways and processes: DNA damage response, altered cell signaling, immune and inflammatory response, and altered metabolic signaling. Category 1 (DNA damage response) DEGs were mainly associated with p53 signaling. Category 2  included DEGs associated with RXR, AHR, nuclear factor-like 2 (NRF2), peroxisome proliferator-activated receptors (PPAR), phosphate and tensin homolog (PTEN), nuclear factor kappa light-activated enhancer of activated B cells (NFkB), lipid, signal transducer and activator of transcription 3 (STAT3), and 5′ adenosine monophosphate-activated protein kinase (AMPK) signaling pathways. Subcategories of cell signaling pathways related to immune/inflammatory response (Category 3) such as acute phase response, T helper cell signaling, and interleukin signaling were also altered. Category 4  included xenobiotic metabolism, glutathione metabolism, and retinol metabolism. In the liver, BaP exposure activated all ten hallmarks of cancer . Pathways common to two other PAHs included p53 signaling, AHR signaling, and retinol metabolism common to BbF and BkF, and lipid signaling common to BbF and DBahA. Although the p53 signaling pathway was altered following treatment with BaP, BbF, and BkF, it was upregulated in response to BaP, whereas it was downregulated in response to BbF and BkF Fig. a. For maFigure A robust transcriptomic response was observed in lungs compared to forestomach and liver for most of the PAHs. Hierarchical cluster analysis was conducted using 2324 DEGs that were differentially expressed in at least one dose group of at least one PAH. Consistent with the observations in the forestomach and liver, transcriptomic patterns were unique in the lungs, exhibiting separate clustering of each PAH along with their dose groups Fig. a. Three Although BaP induced a robust pulmonary transcriptomic response, the main categories of pathways and processes perturbed in the lungs were the same as those in forestomach and livers, with the exception of alterations in homeostasis Fig. d. HoweveAlthough 16 pathways or biological processes were perturbed by BaP and at least one other PAH, the collective pathway response induced by the other PAHs varied significantly, which is clearly shown in Fig. Fourteen upstream regulators were associated with the genes altered in response to three of the PAHs and BaP in lungs (Online Resource 4), including endogenous mammalian chemicals, protein complexes and groups, enzymes, growth factors, ligand-dependent nuclear receptors, and transcription regulators. All of these regulators (except β-estradiol) were connected by a single node—p53—a transcription factor commonly implicated in PAH-induced carcinogenesis.Antibody production, circadian rhythm, calcium signaling, xenobiotic metabolism, G protein signaling, and cytoskeletal organization were among the pathways altered following exposure to PAHs that were not perturbed by BaP exposure Fig. b. AlthoulacZ mutant frequency, and EROD activity for lungs. The purpose is to exclusively compare the PAH-specific responses for these apical endpoints within the most affected tissue.Since the lung transcriptome was the most affected of the three tissues studied, in the present study, we evaluated DNA adduct formation, 32P-post-labeling technique, DNA adducts were measured in mouse lungs following exposure to each of the individual PAHs , and BghiP and IP (ANOVA: P ≤ 0.01). LacZ mutant frequency was marginally increased for BaA and Chr despite the presence of DNA adducts (ANOVA: P ≤ 0.1).A dose-dependent increase in pulmonary We measured enzymatic activity of CYP1A1 via the EROD assay in the lungs of mice exposed to all of the PAHs Fig. c. A doselacZ mutant frequency, and EROD activity) and for the number of hallmarks of cancer activated by each PAH in the forestomach, liver, and lung were obtained. BMD values derived from gene expression data were compared to BMDs of the apical endpoints  for each PAH suggest that the potencies are not statistically significant, with the exception of DBahA, which is substantially lower. The BMD values derived from the The BMDL values derived from the hallmarks of cancer in the forestomach tissue ranged from 25.3 mg/kg-bw/day for BghiP to 150.0 mg/kg-bw/day for Chr, compared to 56.5 mg/kg-bw/day for BaP. The forestomach data for BaA, DBahA, and IP could not be modeled because of lack of response at the transcriptional level. In order of lowest to highest, the BMDL values were BghiP < BaP < BkF < BbF < Chr; however, the lower confidence intervals overlap for all PAHs. The BMD values derived from the hallmarks of cancer in the liver ranged from 17.0 mg/kg-bw/day for DBahA to 71.9 mg/kg-bw/day for BaP. The data for BaA and BghiP could not be modeled due to lack of model fit, and the data for Chr and IP could not be modeled due to lack of response. In order of lowest to highest, the BMD values were DBahA < BkF = BbF < BaP; however, the lower confidence intervals for all PAHs except for DBahA overlap. The BMD values derived from the hallmarks of cancer in the lung ranged from 7.53 mg/kg-bw/day for DBahA to 139.2 mg/kg-bw/day for Chr, compared to 34.5 mg/kg-bw/day for BaP. In order of lowest to highest, the BMD values were DBahA < BkF < IP < BbF < BaP < BaA < Chr. Note that the lower confidence intervals for all PAHs overlap except for DBahA. The data for BghiP could not be modeled due to lack of fit.BaP is commonly accepted as a reference PAH in the estimation of excess lifetime cancer risk posed by genotoxic PAHs or PAH-containing mixtures CCME . This isThe accepted ways by which carcinogenic PAHs induce DNA damage and mutations involve: (1) induction of the dihydrodiol epoxide pathway catalyzed by CYP enzymes leading to stable pro-mutagenic DNA adducts, (2) the oxidation of PAH dihydrodiols by dihydrogenases to PAH-derived ortho-quinones leading to stable DNA adducts and formation of reactive oxygen species that may further contribute to the DNA damage, and (3) binding and activation of AhR resulting in intensified transcriptional activation of not only CYP1A1 but also sulfotransferases and other conjugation enzymes, which also influence DNA adduct formation. The formation of DNA adducts, an immediate consequence of PAH metabolism, is considered an early key event of PAH-mediated carcinogenesis , or gene pathways for regulatory purposes, in the absence of rodent cancer bioassay data, BMDs for these toxicological endpoints may serve to set regulatory limits that would protect against effects that are mechanistically or empirically linked to carcinogenesis and prioritize compounds for further testing by long-term cancer bioassays. Recently, in a comprehensive case study, Moffat et al. , activation of angiogenesis (required for blood vessel formation in tumors), activation of PI3 K/AKT signaling pathway (involved in resisting cell death), cell cycle progression (required for cellular proliferation in the presence of damaged DNA), circadian rhythm signaling (associated with the evasion of growth suppressors), inhibition of PPARγ signaling (required for inhibition of neoplastic growth of cancer cells), avoiding immune destruction, and tumor-promoting inflammation, varied between the PAHs. The other pathways uniquely altered only in response to congener PAHs in the present study, such as calcium signaling , endocytosis , chemokine signaling , and cytoskeletal reorganization , have previously been linked to BaP exposure  Supplementary material 2 (PDF 259 kb)Supplementary material 3 (PDF 26570 kb)Supplementary material 4 (PDF 254 kb)Supplementary material 5 (PDF 274 kb)Below is the link to the electronic supplementary material.