Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions. High-throughput technologies revolutionized biomedical research by enabling comprehensive characterization of biological systems. One of the most common use cases of these technologies is to perform experiments comparing two groups of samples  and identify a list of altered genes. However, this list alone often falls short of providing mechanistic insights into the underlying biology of the disease being studied . TherefoOne approach that reduces the complexity of analysis while simultaneously providing great explanatory power is identifying groups of genes that function in the same pathways, i.e., pathway analysis. Pathway analysis has been successfully and repeatedly applied to gene expression , proteomMost commonly used pathway analysis methods are overrepresentation analysis (ORA) and functional class scoring (FCS). For each pathway, ORA statistically evaluates the proportion of altered genes among the pathway genes against the proportion among a set of background genes. In FCS, a gene-level statistic is calculated using the measurements from the experiment. These gene-level statistics are then aggregated into a pathway-level statistic for each pathway. Finally, the significance of each pathway-level statistic is assessed, and significant pathways are determined.While they are widely used, there are drawbacks to conventional pathway analysis methods. The statistics used by ORA approaches usually consider the number of genes in a list alone. ORA methods are also independent of the values associated with these genes, such as fold changes or p values. Most importantly, both ORA and FCS methods lack in incorporating interaction information. We propose that directly performing pathway analysis on a gene set is not completely informative because this approach reduces gene-phenotype association evidence by ignoring information on interactions of genes.We propose a pathway analysis method, which we named pathfindR, that first identifies active subnetworks and then performs enrichment analysis using the identified active subnetworks. For a given list of significantly altered genes, an active subnetwork is defined as a group of interconnected genes in a protein-protein interaction network (PIN) that predominantly consists of significantly altered genes. In other words, active subnetworks define distinct disease-associated sets of interacting genes. The idea of utilizing PIN information to enhance pathway enrichment results was sought and successfully implemented in numerous studies. Gene Network Enrichment Analysis (GNEA)  analyzesWith pathfindR, our aim was likewise to exploit interaction information to extract the most relevant pathways. We aimed to combine together active subnetwork search and pathway enrichment analysis. By implementing this original active-subnetwork-oriented pathway analysis approach as an R package, our intention was to provide the research community with a set of utilities  that will be effective, beneficial, and straightforward to utilize for pathway enrichment analysis exploiting interaction information.The active-subnetwork-oriented pathway enrichment paradigm of pathfindR can be summarized as follows: Mapping the statistical significance of each gene onto a PIN, active subnetworks, i.e., subnetworks in the PIN that contain an optimal number of significant nodes maximizing the overall significance of the subnetwork, either in direct contact or in indirect contact via an insignificant (non-input) node, are identified. Following a subnetwork filtering step, enrichment analyses are then performed on these active subnetworks. Similar to the above-mentioned PIN-aided enrichment approaches, utilization of active subnetworks allows for efficient exploitation of interaction information and enhances enrichment analysis.For the identification of active subnetworks, various algorithms have been proposed, such as greedy algorithms , simulatIn summary, pathfindR integrates information from three main resources to enhance determination of the mechanisms underlying a phenotype: (i) differential expression/methylation information obtained through omics analyses, (ii) interaction information through a PIN via active subnetwork identification, and (iii) pathway/gene set annotations from sources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) , Reactomhttps://www.R-project.org/) package was developed based on a previous approach developed by our group for genome-wide association studies (GWASes): Pathway and Network-Oriented GWAS Analysis (PANOGA) (The pathfindR R ((PANOGA) . PANOGA (PANOGA) , epileps(PANOGA) , and Beh(PANOGA) . With paIn this article, we present an overview of pathfindR, example applications on three gene expression data sets, and comparison of the results of pathfindR with those obtained using three tools widely used for enrichment analyses: The Database for Annotation, Visualization and Integrated Discovery (DAVID) , SignaliPIN data available in pathfindR by default are KEGG, Biogrid , GeneManThe KEGG Homo sapiens PIN was created by an in-house script using the KEGG pathways. In KEGG, pathways are represented in XML files that contain genes and gene groups, such as protein complexes as entries and interactions as entry pairs. The KEGG pathway XML files were obtained using the official KEGG Application Programming Interface (API) which is a REST-style interface to the KEGG database resource. Using the in-house script, the XML files were parsed; the interactions were added as undirected pairs, while interaction types were disregarded. In cases of an entry in an interacting pair containing multiple genes, interactions from all of these genes to the other entry were built.https://downloads.thebiogrid.org/BioGRID). For Biogrid, Homo sapiens PIN data in tab-delimited text format from release 3.4.156 (BIOGRID-ORGANISM-Homo_sapiens-3.4.156.tab.txt) was obtained from the Biogrid Download File Repository (ftp://ftp.ebi.ac.uk/pub/databases/intact/current) in January 2018. For IntAct, the PIN data in Proteomics Standards Initiative – Molecular Interactions tab-delimited (PSI-MI TAB) (MITAB) format (intact.txt) were obtained from the IntAct Molecular Interaction Database FTP site (http://genemania.org/data/current/Homo_sapiens.COMBINED/). For this PIN only, only interactions with GeneMania weights ≥0.0006 were kept, allowing only strong interactions.For GeneMania, Homo sapiens PIN data in tab-delimited text format from the latest release (COMBINED.DEFAULT_NETWORKS.BP_COMBINING.txt) was obtained from the official data repository . The processing steps performed for all the PINs were (1.) if the HUGO Gene Nomenclature Committee (HGNC) symbols for interacting genes were not provided, conversion of provided gene identifiers to HGNC symbols using biomaRt  was perfGene sets available in pathfindR are KEGG, Reactome, BioCarta, GO-Biological Process (GO-BP), GO-Cellular Component (GO-CC), GO-Molecular Function (GO-MF) and GO-All .https://reactome.org/download/current/). BioCarta gene sets in GMT format were retrieved from the Molecular Signatures Database (MSigDB) (http://software.broadinstitute.org/gsea/msigdb). All “High-quality” GO gene sets were obtained from GO2MSIG (http://www.go2msig.org/cgi-bin/prebuilt.cgi?taxid=9606) in GMT format. All of the datasets were processed using R to obtain (1) a list containing the genes involved in each given gene set/pathway  and (2) a list containing the descriptions for each gene set/pathway .KEGG gene sets were obtained using the R package KEGGREST. Reactome gene sets in Gene Matrix Transposed (GMT) file format were obtained from the Reactome website ((MSigDB)  website  GO2MSIG  web inteAll of the gene sets in pathfindR are for Homo sapiens, and the default gene set is KEGG. The researcher can also use a gene set of their choice following the instructions on pathfindR wiki.All of the default data for PINs and gene sets are planned to be updated annually.–1 is the inverse normal cumulative distribution function. In equation (2), A is the set of genes in the subnetwork and k is its cardinality.In pathfindR, we followed the scoring scheme that was proposed by In the same scoring scheme, a Monte Carlo approach is used for the calibration of the scores of subnetworks against a background distribution. Using randomly selected genes, 2,000 subnetworks of each possible size are constructed, and for each possible size, the mean and standard deviation of the score is calculated. These values are used to calibrate the subnetwork score using equation (3).Currently, there are three algorithms implemented in the pathfindR package for active subnetwork search, described below.Greedy algorithm is the problem-solving/optimization concept that chooses locally the best option in each stage with the expectation of reaching the global optimum. In active subnetwork search, this is generally applied by starting with a significant seed node and considering addition of a neighbor in each step to maximize the subnetwork score. In pathfindR, we used the approach described by new and scorecurrent are the scores of the new and the current solutions, respectively. Finally, temperature is the current temperature.Simulated annealing is an optimization algorithm inspired by annealing in metallurgy. In the annealing process, the material is heated above its recrystallization temperature and cooled slowly, allowing atoms to diffuse within the material and decrease dislocations. Analogous to this process, simulated annealing algorithm starts with a “high temperature” in which there is a high probability of accepting a solution that is worse than the current one as the solution space is explored. The acceptability of worse solutions allows a global search and escaping from local optima. The equation connecting temperature and probability of accepting a new solution is given in equation (4). In this equation, P(Acceptance) is the probability of accepting the new solution. In scoreA less worse solution and higher temperature are the conditions that increase the chance of acceptation of a new solution. The probability of accepting a non-optimal action decreases in each iteration, as the temperature decreases in each step.Simulated annealing provides improved performance over the greedy search by accepting non-optimal actions to increase exploration in the search space. In the active subnetwork search context, the search begins with a set of randomly chosen genes (the chosen genes are referred to as genes in “on” state and the not chosen genes are referred to as genes in “off” state). Connected components in this candidate solution are found, and the scores are calculated. In each iteration, the state of a random node is changed from on to off and vice versa. Connected components are found in the new solution, and their scores are calculated. If the score improves, the change is accepted. If the score decreases, the change is accepted with a probability proportional to the temperature parameter that decreases in each step.Genetic algorithm is a bio-inspired algorithm that mimics evolution by implementing natural selection, chromosomal crossover, and mutation. The main phases of the genetic algorithm are “the selection phase” and “the crossover phase.”In the selection phase, parents from the existing population are selected through a fitness-based process to breed a new generation. Common selection methods are (i) roulette wheel selection in which a solution’s selection probability is proportional to its fitness score, (ii) rank selection in which a solution’s selection probability is proportional to its rank, thus preventing the domination of a high fitness solution to the rest, and (iii) tournament selection in which parents are selected among the members of randomly selected groups of solutions, thus giving more chance to small fitness solutions that would have little chance in other selection methods.In the crossover phase, encoded solution parameters of the parents are exchanged analogous to chromosomal crossover. The common crossover operators are (i) single-point crossover in which the segment next to a randomly chosen point in the solution representation is substituted between parents, (ii) two-point crossover in which the segment between two randomly chosen points is substituted, and (iii) uniform crossover in which each parameter is randomly selected from either of the parents. Mutation is the process of randomly changing parameters in the offspring solutions in order to maintain genetic diversity and explore search space.In our genetic algorithm implementation, candidate solutions represent the on/off state of each gene. In the implementation, we used rank selection and uniform crossover. In each iteration, the fittest solution of the previous population is preserved if the highest score of the current population is less than the previous population’s score. In every 10 iterations, the worst scoring 10% of the population is replaced with random solutions. Because uniform cross-over and addition of random solutions make adequate contribution to the exploration of the search space, mutation is not performed under the default settings.The default search method in pathfindR is greedy algorithm with a search depth of 1 and maximum depth of 1. This method stands out with its simplicity and speed. This is also the “local subnetwork approach” used in the Local Enrichment Analysis (LEAN) method . As mentSimulated annealing and genetic algorithms are heuristic methods that do not make any assumptions on the active subnetwork model. They can let insignificant genes between two clusters of significant genes to create a single connected active subnetwork. Thus, these algorithms may result in a large highest scoring active subnetwork, while the remaining subnetworks identified become small and therefore uninformative. This tendency towards large subnetworks was attributed to a statistical bias prevalent in many tools .The default active search method  in pathfindR was preferred because multiple active subnetworks are used for enrichment analyses. If the researcher decides to use the single highest scoring active subnetwork for the enrichment process, they are encouraged to consider greedy algorithm with greater depth, simulated annealing, or genetic algorithm.The overview of the active-subnetwork-oriented pathway enrichment approach is presented in The required input is a two- or three-column table: Gene symbols, change values as log-fold change  and adjusted p values associated with the differential expression/methylation data.Initially, the input is filtered so that all p values are less than or equal to the given threshold (default is 0.05). Next, gene symbols that are not in the PIN are identified. If aliases of these gene symbols are found in the PIN, these symbols are converted to the corresponding aliases. The processed data are then used for active subnetwork search. The identified active subnetworks are filtered via the following criteria: (i) has a score larger than the given quantile threshold (default is 0.80) and (ii) contains at least a specified number of input genes (default is 10).For each filtered active subnetwork, using the genes contained in each of these subnetworks, separate pathway enrichment analyses are performed via one-sided hypergeometric testing. The enrichment tests use the genes in the PIN as the gene pool . Using the genes in the PIN instead of the whole genome is more appropriate and provides more statistical strength because active subnetworks are identified using only the genes in the PIN. Next, the p values obtained from the enrichment tests are adjusted . Pathways with adjusted p values larger than the given threshold (default is 0.05) are discarded. These significantly enriched pathways per all filtered subnetworks are then aggregated by keeping only the lowest adjusted p value for each pathway if a pathway was found to be significantly enriched in the enrichment analysis of more than one subnetwork.This process of active subnetwork search and enrichment analysis  is repeated for a selected number of iterations .Finally, the lowest and the highest adjusted p values, the number of occurrences over all iterations, and up-regulated and down-regulated genes in each enriched pathway are returned as a table. Additionally, a Hypertext Markup Language (HTML) format report with the pathfindR enrichment results is created. Pathways are linked to the visualizations of the pathways if KEGG gene sets are chosen. The KEGG pathway diagrams are created using the R package pathview . By defaEnrichment analysis usually yields a large number of related pathways. In order to establish representative pathways among similar groups of pathways, we propose that clustering can be performed either via hierarchical clustering (default) or via a fuzzy clustering method as described by Firstly, using the input genes in each pathway, a kappa statistics matrix containing the pairwise kappa statistics, a chance-corrected measure of co-occurrence between two sets of categorized data, between the pathways is calculated .By default, the wrapper function for pathway clustering, cluster_pathways, performs agglomerative hierarchical clustering (defining the distance as 1 − kappa statistic), automatically determines the optimal number of clusters by maximizing the average silhouette width, and returns a table of pathways with cluster assignments.Alternatively, the fuzzy clustering method, previously proposed and described in detail by Finally, the representative pathway for each cluster is assigned as the pathway with the lowest adjusted p value.The researcher can get an overview of the alterations of genes in a pathway via the KEGG pathway graph. To provide even more insight into the activation/repression statuses of pathways per each sample, we devised a simple scoring scheme that aggregates gene-level values to pathway scores, described below.For an experiment values matrix , EM, where columns indicate samples and rows indicate genes, the gene score GS of a gene g in a sample s is calculated as:sdg is the standard deviation for gene g across all samples.Here, Pi, the set of k genes in pathway i, and a sample j, the ith row and jth column of the pathway score matrix PS is calculated as follows:For a set The pathway score of a sample for a given pathway is therefore the average value of the scores of the genes in the pathway for the given sample.After calculation of the pathway score matrix, a heat map of these scores is plotted. Via this heat map, the researcher can examine the activity of a pathway in individual samples as well as compare the overall activity of the pathway between cases and controls.To analyze the performance of pathfindR, we used three gene expression datasets. All datasets were obtained via the Gene Expression Omnibus (GEO) . The firAfter preprocessing, which included log2 transformation and quantile normalization, differential expression testing via a moderated t test using limma  was perfWe chose to use these three datasets because these are well-studied diseases and the involved mechanisms are considerably well characterized. These different datasets also allowed us to test the capabilities of pathfindR on DEGs obtained from different platforms. We performed enrichment analysis with pathfindR, using the default settings. Greedy algorithm for active subnetwork search was used, and the analysis was carried out over 10 iterations. The enrichment significance cutoff value was set to 0.25 for each analysis (changing the argument enrichment_threshold of run_pathfindR function) as we later performed validation of the results using the three significance cutoff values of 0.05, 0.1, and 0.25ith gene ranki was calculated as follows:To better evaluate the performance of pathfindR, we compared results on the three gene expression datasets by three widely used pathway analysis tools, namely, DAVID , SPIA T, and GSEThe unfiltered results of enrichment analyses using the different methods on the three datasets are presented in For each analysis, the Bonferroni-corrected p values for pathfindR were used to filter the results. For all the other tools, as the Bonferroni method would be too strict and result in too few or no significant pathways, the FDR-corrected p values were used.http://cancer.sanger.ac.uk, retrieved on 19.12.2018) were used. These genes will be referred to as “CGC Genes.”Because there is no definitive answer to which pathways are involved in the pathogenesis of the conditions under study, we analyzed the results in light of the existing biological knowledge on the conditions and compared our results with other tools in this context. The significant pathways were assessed on the basis of how well they fitted with the existing knowledge. For this, two separate approaches were taken: (i) assessment of literature support for the significantly enriched pathways (using a significance threshold of 0.05), and (ii) assessment of the percentages of pathway genes that are also known disease genes . While both assessments could be separately used to determine the “disease-relatedness” of a pathway, we chose to use them both as these are complementary measures: the former is a more subjective but a comprehensive measure of association, and the latter is a limited but a more objective measure of association. For determining the percentages of known disease genes in each significantly enriched pathway, two curated lists were used. For the RA dataset, mapped genes in the curated list of SNPs associated with RA was obtained from the NHGRI-EBI Catalog of published genome-wide association studies  . These gWe performed pathfindR analyses using real and permuted data with different sizes to assess the number of enriched pathways identified in the permuted data against the actual data. For this assessment, the RA data was used. The analyses were performed on data subsets taken as the top 200, 300, 400, and 500 most significant DEGs as well as the complete list of 572 DEGs. For each input size, 100 separate pathfindR analyses were performed on both the actual input data and permuted data. While the real input data were kept unchanged, for the permuted data, a random permutation of genes  was carried out at each iteration over 100 analyses. Analyses with pathfindR were performed using the default settings described above.The distributions of the number of enriched pathways for actual vs. permuted data were compared using Wilcoxon rank sum test.We performed ORA as implemented in pathfindR (as the “enrichment” function) to assess any effect of removing DEGs without any interactions on enrichment results. For this purpose, ORA were performed for (i) the full lists of DEGs for all datasets and (ii) the lists of DEGs that are found in the Biogrid PIN. As gene sets, KEGG pathways were used. As background genes, all of the genes in the Biogrid PIN were used for both analyses so that the results could be comparable. The enrichment p values were adjusted using the FDR method. Pathway enrichment was considered significant if FDR was <0.05.To analyze the effect of the chosen PIN on the enrichment results, we performed pathfindR analyses using the four PINs provided by default: the Biogrid, GeneMania, IntAct, and KEGGPINs. For these analyses, the default settings were used with the default active search algorithm (greedy) and the default gene sets (KEGG).https://cran.r-project.org/package=pathfindR . The code of the pathfindR package is deposited in a GitHub repository (https://github.com/egeulgen/pathfindR) along with a detailed wiki, documenting the features of pathfindR in detail. Docker images for the latest stable version and the development version of pathfindR are deposited on Docker Hub (https://hub.docker.com/r/egeulgen/pathfindr) The pathfindR package is freely available for use under MIT license: A total of 572 DEGs were identified for the RA dataset (The summary of results obtained using the different tools and literature support for the identified pathways (where applicable) are presented in Clustering allowed us to obtain coherent groups of pathways and identify mechanisms relevant to RA, including autoimmune response to the spliceosome , mechaniThe activity scores of the representative pathways for each subject indicated that most representative pathways were down-regulated in the majority of subjects  are presented in Upon clustering, 14 clusters were identified , apoptosRepresentative pathways that were upregulated in the majority of subjects included important pathways related to cancer in general and colorectal cancer, such as the proteoglycans in cancer, adherens junction, gap junction, and Hippo signaling pathway. Representative pathways that were downregulated in the majority of subjects included other important pathways related to colorectal cancer, such as valine, leucine, and isoleucine degradation, mTOR signaling pathway, and cell cycle  are presented in The clusters identified by pathfindR pointed to several mechanisms previously shown to be important for prostate cancer. These mechanisms included but were not limited to the prostate cancer pathway and related signaling pathways , cancer The majority of representative pathways relevant to PCa were down-regulated  were identified (The percentages of disease-related genes for each pathway found to be enriched by any tool (adjusted-p ≤ 0.05) are presented in the corresponding columns of The distributions of disease-related gene percentages in pathways identified by each tool in the three different datasets, filtered by the adjusted-p value thresholds of 0.05, 0.1, and 0.25, are presented in To assess the number of pathways identified to be enriched by pathfindR, we performed analyses using actual and permuted data of different sizes. Comparison of the distributions of actual vs. permuted data is presented in It was observed that the ratio of the median number of pathways  tended to increase as the number of input genes increased. This is most likely because as the input size gets larger, there is higher chance in finding highly connected subnetworks that in turn leads to identifying a higher number of enriched pathways.To gain further support for our proposal that directly performing enrichment analysis on a list of genes is not completely informative because this ignores the interaction information, we performed ORA (as implemented in pathfindR) on (i) all of the DEG lists  and (ii) the filtered list of DEGs for the same datasets so that they only contain DEGs found in the Biogrid PIN. This allowed us to assess any effect of eliminating DEGs with no interactions on the enrichment results.The numbers of DEGs found in the Biogrid PIN for each dataset was as follows: RA—481 , CRC—989  and PCa—900 . The ORA results are presented in To assess any effect of the choice of PIN on pathfindR results, we first compared the default PINs in terms of the interactions they contain. The number of interactions in the PINs were as follows: 289,417 interactions in Biogrid, 79,741 interactions in GeneMania, 121,007 interactions in IntAct, and 53,047 interactions in KEGG. The numbers of common interactions between any pair of PINs and the overlap percentages of the interactions are presented in We then proceeded with analyzing any effect of the choice of PIN on active-subnetwork-oriented pathway enrichment analysis. Venn diagrams comparing enrichment results obtained through pathfindR analyses with all available PINs are presented in As described in Materials and Methods, the results presented in this subsection were obtained using greedy search with search depth of 1 and maximum depth of 1, which results in multiple subnetworks structured as local subnetworks. Although it is not fully dependent on it, this method requires direct interactions between input genes. In the extreme case where there is no direct connection between any pair of two input genes, it is impossible to get any multi-node subnetworks with this method. Therefore, in order to gain a better understanding of the lack of overlap between the enrichment results presented above, we analyzed the numbers of direct interactions of input genes in each PIN. These results are presented as Venn diagrams in In case of utilizing KEGG PIN and KEGG pathways, the same interactions for both subnetwork interaction and enrichment analysis are considered. This approach does not introduce any extra information to the analysis, and it is clear that interacting gene groups in the KEGG PIN will be enriched in KEGG pathways. This explains the high number of pathways obtained using the KEGG PIN. Moreover, it is known that pathways in pathway databases may be strongly biased by some classes of genes or phenotypes that are popular targets, such as cancer signaling . TherefoConsidering all of the above-mentioned findings, we conclude that utilizing the Biogrid PIN can provide the researcher with the most extensive enrichment results.PathfindR is an R package that enables active subnetwork-oriented pathway analysis, complementing the gene-phenotype associations identified through differential expression/methylation analysis. In most gene set enrichment approaches, relational information captured in the graph structure of a PIN is overlooked. Hence, during these analyses, genes in the network neighborhood of significant genes are not taken into account. The approach we considered for exploiting interaction information to enhance pathway enrichment analysis was active subnetwork search. In a nutshell, active subnetwork search enables inclusion of genes that are not significant genes themselves but connect significant genes. This results in the identification of phenotype-associated connected significant subnetworks. Initially identifying active subnetworks in a list of significant genes and then performing pathway enrichment analysis of these active subnetworks efficiently exploits interaction information between the genes. This, in turn, helps uncover relevant phenotype-related mechanisms underlying the disease, as demonstrated in the example applications.Through pathfindR, numerous relevant pathways were identified in each example. The literature-supported disease-related pathways mostly ranked higher in the pathfindR results. The majority of additional pathways identified through pathfindR were relevant to the pathogenesis of the diseases under study, as supported by literature. A separate confirmation of disease-relatedness was provided by analysis of the distributions of the percentage of disease genes in the identified pathways. This analysis revealed that pathfindR pathways contained the highest median percentages of disease-related genes in each dataset regardless of significance cutoff value, implying that the pathways identified by pathfindR are indeed associated with the given disease. Together, these two assessments of disease-relatedness of pathways indicate that pathfindR produces pathway enrichment results at least as relevant as the other tools widely used for enrichment analysis.We propose that pathfindR performed better than the analyzed pathway analysis tools because, for enrichment analysis, it included disease-related genes that were not in the DEG list but that were known to interact with the DEGs, which most enrichment tools disregard. By performing enrichment analyses on distinct sets of interacting genes , pathfindR also eliminated “false positive” genes that lacked any strong interaction. The above findings indicate that incorporating interaction information prior to enrichment analysis results in better identification of disease-related mechanisms.This package extends the use of the active-subnetwork-oriented pathway analysis approach to omics data. Additionally, it provides numerous improvements and useful new features. The package provides three active subnetwork search algorithms. The researcher is therefore able to choose between the different algorithms to obtain the optimal results. For the greedy and simulated annealing active subnetwork search algorithms, the search and enrichment processes are executed several times. By summarizing results over the iterations and identifying consistently enriched pathways, the stochasticity of these algorithms is overcome. Additionally, the researcher is able to choose from several built-in PINs and can use their own custom PIN by providing the path to the SIF file. The researcher is also able to choose from numerous built-in gene sets, listed above, and can also provide a custom gene set resource. pathfindR also allows for clustering of related pathways. This allows for combining relevant pathways together, uncovering coherent “meta-pathways” and reducing complexity for easier interpretation of findings. This clustering functionality also aids in eliminating falsely enriched pathways that are initially found because of their similarity to the actual pathway of interest. The package also allows for scoring of pathways in individual subjects, denoting the pathway activity. Finally, pathfindR is built as a stand-alone package, but it can easily be integrated with other tools, such as differential expression/methylation analysis tools, for building fully automated pipelines.To the best of our knowledge, pathfindR is the first and, so far, the only R package for active-subnetwork-oriented pathway enrichment analysis. It also offers functionality for pathway clustering, scoring, and visualization. All features in pathfindR work together to enable identification and further investigation of dysregulated pathways that potentially reflect the underlying pathological mechanisms. We hope that this approach will allow researchers to better answer their research questions and discover mechanisms underlying the phenotype being studied.OS, OO, and EU conceived the pathway analysis approach. OO and EU implemented the R package. EU performed the analyses presented in this article. OUS, OO, and EU interpreted the results. All authors were involved in the writing of the manuscript and read and approved the version being submitted.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There is little understanding of the molecular processes involved in the pathogenesis of osteoarthritis, limiting early diagnosis and effective treatment of OA. Use of genechips can provide insights into the molecular pathogenesis of diseases. In this study, determination of gene expression profiles of osteoarthritis peripheral blood mononuclear cells will allow exploration of the molecular pathogenesis of OA and find out more candidate biomarkers and potential drug targets of OA. PIK3CA, PIK3CB, PIK3CD, PIK3R1, MAPK14, IL1A, JUND, FOSL2, and PPP3CA were the gene symbols of the osteoclast differentiation pathway which was the most significant pathway enriched by DEGs. However, the MAPK signaling pathway occupied the core position of all the pathways which can regulate apoptosis, cell cycle, wnt signaling pathway, p53 signaling pathway, and phosphatidylinositol signaling system. Furthermore, PI3Ks may regulate IL1A, JUND, FOSL2 and PPP3CA through the MAPK signaling pathway.  A total of 1231 DEGs were screened out including 791 upregulated DEGs and 440 downregulated DEGs. The most significant upregulated DEG was RPL38, which may inhibit chondrocyte differentiation and synthesis of the extracellular matrix.  These identified DEGs and pathways may be novel biomarkers to monitor the changes of OA and can be a potential drug target for the treatment of OA. Osteoarthritis (OA) is a chronic degenerative joint disease characterized by degeneration of articular cartilage, synovium inflammation, imbalance in the synthesis and catabolism of the extracellular matrix of chondrocytes, and the formation of subchondral bone and osteophytes . OA is cTogether with the Human Genome Project and the rapid development of molecular biology technology, high-throughput genechip technology has emerged, allowing the rapid and simultaneous analysis of thousands of gene loci . Use of In this study, gene expression profiles of osteoarthritis PBMCs were constructed by genechip technology. Differentially expressed genes (DEGs) were screened out by comparing the genechip results of osteoarthritis patients with those of normal controls. To obtain greater insights into the molecular mechanisms of OA, we applied bioinformatics analysis. Gene ontology (GO) analysis and pathway enrichment analysis were performed for DEGs on the Gene-Cloud of Biotechnology Information (GCBI) bioinformatics platform, revealing the core genes and signaling pathways in the pathogenesis of OA. In addition, the network relationships between DEGs and signaling pathways were determined by pathway relation and gene signal network analyses, revealing key players in the molecular pathogenesis of OA.P value. The top thirty up- and downregulated DEGs are listed in P value of the upregulated DEGs was ribosomal protein L38 , followed by protein phosphatase 3, catalytic subunit, alpha isozyme . The most significant downregulated DEG was IKAROS family zinc finger 1 , followed by chromosome 7 open reading frame 43 . Cluster analysis was performed on the differentially expressed genes, and the result is represented as a heatmap , interleukin 6 receptor (IL6R), interleukin 7 receptor (IL7R), protein phosphatase 3, catalytic subunit, alpha isozyme (PPP3CA), ribosomal protein L38 (RPL38), phosphatidylinositol-4,5-bisphosphate 3-kinase (PIK3), mitogen-activated protein kinase 14 (MAPK14), tumor necrosis factor superfamily, member 10 (TNFSF10), tumor necrosis factor superfamily, member 13 (TNFSF13), activating transcription factor 2 (ATF2), and others. The DEGs mapped to 143 pathways, and the pathway enrichment analysis is partly presented in Tyrosine kinase 2 (TYK2), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit beta (PIK3CB), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit delta (PIK3CD), phosphoinositide-3-kinase, regulatory subunit 1 (PIK3R1), MAPK14, IL1A, jun D proto-oncogene (JUND), FOS-like antigen 2 (FOSL2), and PPP3CA genes are related to osteoclast differentiation, and IL-1A, JUND, PPP3CA, and MAPK14 are involved in the MAPK signaling pathway.We next performed GO analysis of the identified DEGs and found 449 biological processes that were significantly enriched. The 30 most enriched GO terms and related DEGs are shown in phospholipase C, beta 1 (PLCB1), PIK3CA, PIK3CB, PIK3CD, PIK3R1, v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS), MAPK14, protein kinase, cAMP-dependent, catalytic, beta (PRKACB), phosphatidylinositol-4-phosphate 5-kinase, type I, alpha (PIP5K1A), G protein, alpha inhibiting activity polypeptide 2 (GNAI2), G protein, alpha inhibiting activity polypeptide 3 (GNAI3), calmodulin 1 (CALM1), G protein, beta polypeptide 2 (GNB2), G protein, gamma 11 (GNG11), beta-actin (ACTB), and Rac family small GTPase2 (RAC2) interact with more than 10 DEGs, making them core genes in the network. The network relationship of DEGs related to osteoclast differentiation, including PIK3CA, PIK3CB, PIK3CD, PIK3R1, MAPK14, JUND, FOSL2, IL1A, and PPP3CA, is shown in The geneSignalNetwork analysis was performed on 1231 DEGs and revealed interactions between 183 DEGs . The resThe pathwayRelationNetwork analysis was also performed and revealed interaction relationships between 68 pathways . The MAPp value were RPL38, PPP3CA, TROVE domain family, member 2 (TROVE2), PRP40 pre-mRNA processing factor 40 homolog A (PRPF40A), centrosomal protein 350 kDa (CEP350), IKAROS family zinc finger 1 (IKZF1), mitochondrial ribosomal protein S31 (MRPS31), RAB11A, member RAS oncogene family (RAB11A), zinc finger CCCH-type containing 14 (ZC3H14), and chromosome 7 open reading frame 43 (C7orf43). The most significant upregulated DEG was RPL38, which affects processes of gene expression, DNA transcription, gene translation, and cell proliferation. Normal expression of ribosomal protein L29 (RPL29) is essential for chondrocyte proliferation and skeletal growth, and high expression level of RPL29 inhibited chondrocyte terminal differentiation, keeping cells in a state of abnormal proliferation [In the present study, the Human Genome U133A genechip from Affymetrix was used to detect the gene expression profiles of PBMCs from 19 patients with osteoarthritis and 22 healthy controls. We identified 1231 DEGs, with 791 upregulated DEGs and 440 downregulated DEGs. The top 10 most significant DEGs according to the PIK3CA, PIK3CB, PIK3CD, PIK3R1, MAPK14, IL1A, JUND, FOSL2, and PPP3CA were the gene symbols of osteoclast differentiation pathway, and these genes showed increased expression in PBMCs of OA patients compared to normal controls. This suggests that mononuclear cells from patients with OA have stronger osteoclast differentiation ability. PIK3CA, PIK3CB, PIK3CD, and PIK3R1 belong to the PI3Ks (phosphoinositide-3-kinases) family [Tcirg1 to increase osteoclastogenesis [2+/calmodulin [Osteoclasts are closely related to bone resorption and subchondral bone remodeling processes involved in the pathophysiology of OA . Osteocl) family . PI3K is) family . PI3K in) family . The PI3) family . JUND an) family . Fos is ) family . The JUNogenesis . IL-1A, ogenesis . PPP3CA,lmodulin . PPP3C hlmodulin . The deplmodulin . CollectThe most significant pathway revealed by network analysis was the MAPK signaling pathway, with an occupied core position in the network. The MAPK signaling pathway as the source pathway can regulate apoptosis, cell cycle, wnt signaling pathway, p53 signaling pathway, and the phosphatidylinositol signaling system. MAPKs are serine/threonine protein kinases, and activating a protein kinase cascade (MAPKKK-MAPKK-MAPK) allows the transduction of extracellular signals into the nucleus. In this way, the expression of genes related to cell proliferation, differentiation, apoptosis, and stress response can be regulated . The MAPβ), cell cycle (TGF-β and SMAD2), cell apoptosis (CASP and BAX). Secondly, high expression of proteinase (ADAMTS10) is same. Thirdly, some signal pathways enriched by DEGs are the same  [The results of this study have similarities and differences with published microarray analysis on synovial fluid, synovium membranes, subchondral bone, and cartilage. Firstly, the biological processes enriched by some DEGs are the same, such as immune response and inflammatory response  –8. Howevpathway)  showed tPIK3CA, PIK3CB, PIK3CD, PIK3R1, MAPK14, IL1A, JUND, FOSL2, and PPP3CA were the gene symbols of the osteoclast differentiation pathway. These genes promote the development of OA through increasing osteoclastogenesis. PI3Ks may also regulate IL1A, JUND, FOSL2, and PPP3CA through the MAPK signaling pathway. The discovery of these molecular mechanisms associated with the progression of osteoarthritis may contribute to the early diagnosis and treatment of osteoarthritis. Further work should evaluate the utility of these DEGs as biomarkers with diagnostic efficiency, as well as their potential as novel drug targets for the treatment of OA.Gene expression profiles were determined for PBMCs in OA patients and healthy controls. Analysis revealed 1231 DEGs, with 791 upregulated DEGs and 440 downregulated DEGs. The most significant upregulated DEG was RPL38, which may inhibit chondrocyte differentiation and synthesis of the extracellular matrix. The research subjects consisted of 19 OA patients and 22 healthy controls. All the research subjects were collected from Department of Rheumatology and Immunology, the second Xiangya Hospital of Central South University. The symptomatic primary OA patients who met the inclusion criteria were diagnosed by American College of Rheumatology(ACR) standard diagnostic criteria , with thg). Transferring the upper aqueous phase to another RNase-free EP tube and adding isopropanol, the tube was placed stably for 10 min and then centrifuged (15 min at 12 000g). The sediments (RNA) were washed with 75% ethanol and dissolved in DEPC-treated water. Quality and quantity of the isolated RNA were measured by analyzing samples using the 2100 Bioanalyzer  and Nanodrop spectrophotometer.We collected fresh peripheral blood from each subject and separated circulating mononuclear cells (MNCs) with UNI-SEP separation tube  according to manufacturer's recommendations. UNI-SEP products are sterile plastic centrifuge tubes containing a solution of 5.6% polysucrose and 9.6% sodium metrizoate. Then, we used monocyte negative isolation kit  to isolate monocytes from MNCs. The antibody mix contains mouse IgG antibodies for CD2, CD7, CD16, CD19, CD56 and CD235a bound the surface of depletion dynabeads, and we discarded beads with unwanted cells and collected untouched monocytes. Then, the monocytes were lysed in TRIzol Reagent  for 5 min, chloroform was added, and then centrifuged  amplification and biotin labeling and was cleaned up and fragmented. All the operations were following the instructions of MessageAmp™ Premier RNA Amplification kit . Subsequently, the biotinylated cRNAs were hybridized to Affymetrix Human Genome U133A genechip overnight at 45°C with rocking. Genechip was then washed and stained according to the protocol of GeneChip® Hybridization, Wash, and Stain kit . Finally, Affymetrix Gene Array Scanner G2500A scanned the chip, and the scanned images were analyzed by MAS5.0 software.Total RNA was reverse transcribed to synthesis first-strand cDNA by using a T7-Oligao (dT) Promoter Primer, and then second-strand cDNA was synthesized and purified. Next, in https://www.gcbi.com.cn/, GMINIX Informatics Ltd. Co, Shanghai, P.R. China). The original data CEL files were imported into the two groups. Using SAM  R program [Q < 0.05, P < 0.05, and fold change > 1.2. Hierarchical clustering (a heatmap) was performed with these screened DEGs to identify whether they can make an obvious distinction between OA samples and control samples.The image signals of the chip were transformed into the original data of gene expression level by MAS5.0 software. OA group and control group were set up on the GCBI working platform  and pathway enrichment analysis (P < 0.05) for DEGs on the GCBI working platform. In GO analysis, biological processes, cell components, and molecular processes are mainly involved. In the screening of DEGs, more information is provided by biological processes. Therefore, in GO analysis on the GCBI platform, biological processes should be selected as the “analysis type” in parameter setting.We also performed GO (gene ontology) analysis (For DEGs and its associated signaling pathways, we did geneSignalNetwork and pathwayRelationNetwork analysis on the GCBI platform, so the interaction relationship of DEGs and signaling pathways was obtained.


Natural products are the source of various functional materials such as medicines, and understanding their biosynthetic pathways can provide information that is helpful for their effective production through the synthetic biology approach. A number of studies have aimed to predict biosynthetic pathways from their chemical structures in a retrosynthesis manner; however, sometimes the calculation finishes without reaching the starting material from the target molecule. In order to address this problem, the method to find suitable starting materials is required.In this study, we developed a predictive workflow named the Metabolic Disassembler that automatically disassembles the target molecule structure into relevant biosynthetic units (BUs), which are the substructures that correspond to the starting materials in the biosynthesis pathway. This workflow uses a biosynthetic unit library (BUL), which contains starting materials, key intermediates, and their derivatives. We obtained the starting materials from the KEGG PATHWAY database, and 765 BUs were registered in the BUL. We then examined the proposed workflow to optimize the combination of the BUs. To evaluate the performance of the proposed Metabolic Disassembler workflow, we used 943 molecules that are included in the secondary metabolism maps of KEGG PATHWAY. About 95.8% of them (903 molecules) were correctly disassembled by our proposed workflow. For comparison, we also implemented a genetic algorithm-based workflow, and found that the accuracy was only about 52.0%. In addition, for 90.7% of molecules, our workflow finished the calculation within one minute.The Metabolic Disassembler enabled the effective disassembly of natural products in terms of both correctness and computational time. It also outputs automatically highlighted color-coded substructures corresponding to the BUs to help users understand the calculation results. The users do not have to specify starting molecules in advance, and can input any target molecule, even if it is not in databases. Our workflow will be very useful for understanding and predicting the biosynthesis of natural products. Plants biosynthesize various natural products that protect them from being eaten by herbivores. Herbivore insects can detoxify these phytotoxic molecules and can biosynthesize various molecules including pheromones. Fungi are also a source of valuable natural products such as antibiotics. Secondary metabolites are species-specific natural products that are not directly involved in the species survival. Secondary metabolites and their derivatives are used widely in medicines, cosmetics, and agriculture. For example, penicillin, which was discovered by Fleming in 1928 [Saccharomyces cerevisiae. The biosynthesis need not necessarily mimic the actual biosynthetic pathway of the target molecule, but knowing the actual metabolic pathway can be very useful when constructing the pathway in a model organism.Identifying the biosynthetic route of natural products is not only of pharmacological interest, but is also required by engineers and biologists who use synthetic biology techniques –8. SynthHowever, although the chemical structures of a large number of metabolites are known, the metabolic pathways that lead to their synthesis are not yet known. Moreover, the recent development of high-throughput sequencers and metabolome analysis technology has revealed a large number of natural products for which the biosynthetic metabolic pathways are unknown. Currently, the identification of relevant enzymes in a putative pathway is carried out by trial and error based on knowledge and experience; therefore, an information science-based approach is highly desirable.A large variety of natural products are produced from a limited variety of starting materials . In thisIn organic synthesis, retrosynthetic analysis is used to design the synthetic route of a target molecule. The retrosynthetic analysis method uses the target molecule and constructs the route backward from the target molecule to the starting material. A similar approach can be applied to metabolic pathway prediction; however, the number of iterations required to generate all virtual reactions can easily explode, making it difficult to find an optimal solution in a finite time. In a previous study, machine learning was applied to decide whether a pair of metabolite molecules could form an enzymatic reaction, thereby showing the potential to predict the metabolic pathway . AnotherIn this study, we developed a workflow named the Metabolic Disassembler to automatically identify the BUs that corresponded to the starting materials of a given natural product. One of the important characteristics is that our method “disassembles” a given molecule into substructures that imply the starting materials. The aim was to provide support for identifying biosynthesis pathways. We evaluated the performance of the Metabolic Disassembler using datasets of secondary metabolites acquired from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database , and shoThe goal of the Metabolic Disassembler is not to construct artificial pathways, but is to know the actual metabolic pathways of natural products. In order to achieve our goal, compounds and reactions in primary  metabolism are not needed. Rather, they are a hindrance to the calculation. Also, if a secondary metabolite is not associated with known biosynthetic pathway, no one would estimate if the prediction is true. Therefore, we need to collect secondary metabolites for which biosynthetic pathways are already known. We surveyed databases including MetaCyc  and Rhea2 and C5 units. This means that it is not difficult to identify these BUs because they are already quite obvious, and it is more important to analyze how the BUs form complex rings. Therefore, in this study, we focused on the 1.10 class, which contains 1111 molecules in 28 metabolic pathway maps. Molecules that had an “R” group, indicating they contained undefined structures, caused errors when using RDKit [In KEGG, metabolic compounds are given identifiers that contain the letter “C” and a five-digit number . We also used KEGG PATHWAY secondary metabolism maps to evaluate the accuracy of disassembly. In KEGG PATHWAY, secondary metabolism is classified as 1.9 “Metabolism of terpenoids and polyketides” (hereafter referred to as the 1.9 class) and 1.10 “Biosynthesis of other secondary metabolites” (hereafter referred to as the 1.10 class). The 1.9 class contains a group of molecules that are biosynthesized through mevalonate or non-mevalonate pathways, and BUs in these pathways are mostly limited to Cng RDKit , and theWe implemented our application software using Python programming language (version 3.6.6) on Anaconda3 (version 5.3.0) , Network2 unit in the malonic acid pathway and the C5 unit in the isoprenoid pathway. The goal of this study was to identify the starting materials and to provide information that can be used to predict the biosynthesis pathway; therefore, it is more useful practically to deal with substructures that are larger than the minimal units, which correspond to the metabolites located near the boundary area of primary and secondary metabolism. We defined three types of biosynthetic units (BUs), basic BUs (BBUs), derivative BUs (DBUs), and preferential BUs (PBUs), and stored them in a biosynthetic unit library (BUL).Some minimal units for the biosynthesis of natural products are known, including the acetate-CThe BBUs were defined as important intermediates in biosynthetic pathways and included the molecules in the starting point of a pathway map, the molecules at the branches of a pathway, and the molecule generated immediately after a large structural change. Examples of BBUs are shown in Fig. Some substructures, such as the glucose residue in glucosides, remain unchanged downstream in the biosynthetic pathway. To avoid an unnecessary increase in computational costs, we defined a set of PBUs that contained five monosaccharide residues, a shikimic acid moiety, and a betalamic acid moiety Fig. .Fig. 2SThe workflow consists of five steps: (1) input the target molecule as a query, (2) generate a query-specific BUL, (3) generate the fragment network, (4) generate and sort the BU combination candidates, and (5) output the calculation result.First, a user inputs the target molecule as a query. Acceptable file formats are Molfile, SMILES (Simplified Molecular Input Line Entry System) , InChI self, (Mol)query, useChirality = True).True if the query contains the given substructure, and returns False if not. Stereoisomers are distinguished, as shown in the example in Fig. This method returns rdkit.Chem.rdFMCS.FindMCS.The FindMCS method finds the maximum common substructure (MCS) in two or more molecules. Using the options as described above, bond orders such as aromatic and double bonds are ignored, thereby enabling an ambiguous MCS search. However, allowing too many ambiguous bonds produces an excessive number of candidate units. Therefore, the Metabolic Disassembler rescues a substructure only if the ratio of the number of ambiguous bonds to conjugated double bonds in the resulting MCS is ≤0.2. After collecting the units for the query-specific BUL, the units are sorted in decreasing order of the number of the atoms they contain.Third, the query molecule is divided repeatedly until every fragment matches a BU in the query-specific BUL, and a fragment network is generated to represent the relationship between the query molecule and the obtained fragments Fig. . The frardkit.Chem.GetSubstructMatch((Mol)self, (Mol)query, useChirality = True).The flowchart used to obtain the fragment network has two stages Fig. . In the n = ⌈ N/2 ⌉ (1).An example of a fragment network produced by the above process is shown in Fig. Fourth, using the parent-child relationship, a parent fragment can be replaced by child fragments, and this process can be iterated using the queue data structure Fig. . The frainit = ⌈ N/k ⌉ (2).limitN ≤ 4; 3 if 5 ≤ N ≤ 24; 4 if 25 ≤ N ≤ 53; 6 if 54 ≤ N}.where, k = {1 if If every fragment is identical to one of the BUs in the query-specific BUL, the combination is added to the candidate list. The obtained candidate combinations are sorted in descending order of the size of the largest fragment as the first priority, and ascending order of the number of fragments as the second priority. An example of a sorted candidate list is shown in Table Finally, to easily understand the optimized BU combination, the Metabolic Disassembler outputs a colored image describing the fragmentation, as shown in the example in Fig. Since none of previous studies aimed to find starting materials of secondary metabolites directly, it is difficult to show the performance of our proposed workflow. Here, the performance of our proposed workflow, which is a knowledge-based system, was compared with that of a baseline approach using a genetic algorithm, which is not knowledge-based. We used the Python library named DEAP  version, as explFirst, N individuals are generated randomly as the first generation, where an individual is represented as a binary (0 or 1) vector that represents whether or not the corresponding bond should be digested. The fitness function decides whether an individual survives, and those that survive are subjected to any of three operations, namely the crossover of two individuals, mutation of an individual, or simply the copy of an individual. The generated N individuals are referred to as the next generation. This process is repeated until the predetermined ending condition is reached, and the individuals with highest fitness at the final generation are regarded as the best solution.In this study, we set the population  to 300, the crossover probability to 80%, and the mutation probability to 0.5%. The fitness function was defined as the minimum similarity between the fragments and the BUs in the query-specific BUL. We used the Tanimoto coefficient of the Morgan fingerprint   as the sA summary of the ratio of correct disassembly for the respective pathway maps is shown in Table We measured the computational time for the Metabolic Disassembler with the 943 molecules, executed in the computing environment described in Methods. We performed the calculation five times for each molecule and the obtained average values were recorded , phenazine-1,6-dicarboxylic acid (C12119), and papaverine (C06533), were incorrectly disassembled. We decided not to apply this rule if the number of aromatic bonds in the query molecule was ≤16 and because of this, six molecules, anhydroglycinol (C10200), 2-heptyl-4-quinolone (C20643), 2-heptyl-3-hydroxy-4-quinolone (C20643), coumarin (C05851), scopoletin (C01752), and scopolin (C01527), were incorrectly disassembled. Among the nine incorrectly disassembled molecules, coumarin, scopoletin, scopolin, coumestrol, and papaverine were correctly disassembled by the baseline approach, as explained above. However, the other four molecules were incorrectly disassembled by both workflows. We used the FindMCS method in RDKit to allow ambiguous matching, but this method easily leads to an increase in the calculation time and a decrease in interpretability. Additionally, the five molecules in map00960, slaframine (C06185), 13-(2-methylcrotonoyl)oxylupanine (C04170), 13-hydroxylupanine (C02621), pseudopelletierine (C10865), and calystegin A3 (C10850), were incorrectly disassembled despite of the presence of relevant BUs. This was caused by the GetSubstructMatch method in RDKit, which returns only the first found substructure even if theoretically there are more substructures, as shown in the example in Fig. Some of the other unsuccessful disassembles were caused by rearrangements of the ring structures. For example, the structures of the fused rings in aflatoxin B1 (C06800), B2 (C16753), G1 (C16755), and G2 (C16754) were different from those of the respective precursors, which caused the incorrect disassembly. This problem could be remedied by adding more substructures to the BUL; however, this is not realistic.We introduced PBUs with the aim of shortening the calculation time, especially for molecules that contain sugar residues. We also compared the algorithms that did and did not use PBUs, taking lampranthin II (C08552), which has a betalamic acid (C08538) moiety and saccharide residues, as an example Fig. . The comIn this study, we did not focus on terpenoids and polyketides, which were mainly in the metabolism map 1.9 class. The appropriate BUs of terpenoids and polyketides would be acetyl-CoA and so on, however, adding these BUs could not solve the problem. In this and other studies, manual curations are important for good performance. BBUs and DBUs were manually selected in order to appropriately disassemble molecules, and PBUs were selected in order to shorten the unnecessary computational time. Enhancement of the BUL could improve the coverage but may increase the incorrect substructure matches. We already examined our manual curation, and the current BUs are the best at this moment. When it becomes apparent that some other secondary metabolites originate from other pathways, the enhancement of BUs may work well.2 units that originate from malonyl-CoA (C00083). However, this molecule was excluded from the calculation because the number of fragments exceeded the upper limit.In fact, the 1.10 class also contains some terpenoids and polyketides and our proposed workflow produced incorrect disassembles for them. The four molecules in map000333, 2-methyl-3-n-amyl-dihydropyrrole (C21571), 2-methyl-3-n-amyl-pyrrole (C21572), 4-keto-2-undecylpyrroline (C21573), and 2-undecylpyrrole (C21574), are generated by amination and cyclization of fatty acids, but do not originate from L-proline as incorrectly indicated by our proposed workflow. Similarly, for the fatty acid dodecanoic acid (C02679) in map000333, the desired combination is the six C7 units derived from octanoyl-CoA (C01944), it is necessary to prepare 19 BUs in which one of the six C–C bonds becomes a double bond. There are 33 combinations for the C8 units. Therefore, preparing for other Cn units produces an enormous number of BUs and slows the processing speed. In addition, if many straight-chain alkyl groups are registered in the BUL, molecules that did not originate from acetate-malonic acid pathway are adversely affected. Therefore, we concluded that the BUL and our proposed workflow should not be applied to the biosynthesis of terpenoids and polyketides.The BUL could not support cases where the number of atoms decreased in the BU or when a double bond was generated in a BU by cyclization and subsequent oxidation. For example, to cope with the CDespite the efforts to enhance the BUL to cover a wide range of natural products, there were still nine molecules that were not covered. Apramycin (C01555) and oxyapramycin (C17997) in map00524 are synthesized via paromamine (C01743; Fig. Although enhancement of the BUL could be a solution, its expansion may increase the number of incorrect substructure matches as well as the calculation time. A sufficient number of BUs is necessary for successful disassembly, therefore, it is necessary to strengthen the BUL while considering its tradeoff.The Metabolic Disassembler disassembles target chemical structures into relevant biosynthetic units that correspond to their starting materials, which is the first step in predicting the biosynthetic pathways of natural products. The users can use the Python program as well as the BUL so that the users do not have to reproduce this knowledge-based system. The Metabolic Disassembler will also help to identify the chemical bonds generated during the biosynthetic pathway, thereby providing valuable information for predicting the biosynthetic pathway of natural products.Additional file 1. Summary of disassembly calculation for each map. Detailed explanation of disassembly calculation for each pathway map.


Radiation recall dermatitis (RRD) associated with actinomycin D was first described by in 1959, followed by the reporting of several RRD-inducing drugs. In 1994, a study demonstrated docetaxel-induced RRD for the first time; however, despite some case studies reporting RRD, a little has been reported on it since then. Here we present a rare case of atypical docetaxel-induced RRD.2) every 5 weeks in consideration of myelosuppression for hormone-resistant multiple metastases. Although lung metastasis shrunk by one cycle docetaxel, radiotherapy for the thoracic and lumbar vertebrae was performed for back pain and lumbago. On day 21, at the end of radiotherapy, the same dose of docetaxel was administrated for the third time. On day 7, after third docetaxel administration, erythema appeared in a irradiated field of the thoracic and lumbar vertebra. Erythema primarily appeared on the anterior side of the body, and no skin reaction was noted on the posterior part of the thoracic irradiated area. Notably, no skin reaction was observed in the previously irradiated field for prostate cancer.The patient in his 60s was administered radiotherapy for high-risk prostate cancer. He continued receiving hormonal therapy for 2 years because of being in a high-risk group and became nadir. Six months since the completion of hormonal therapy, his prostate-specific antigen (PSA) level increased again. Based on the radiological examination, he was diagnosed with multiple lung, bone, and lymph node metastases. Accordingly, we started docetaxel (75 mg/mThis case report draws attention to the development of atypical RRD after administration of docetaxel and advises careful follow-up even if RRD does not appear after the first docetaxel administration. Furthermore, the lack of comprehensive investigation of docetaxel-induced RRD renders its frequency and characteristics unclear. Docetaxel is widely used as a treatment regimen for head and neck, nonsmall cell lung, and prostate cancer. With a recent increase in docetaxel administration to treat several other types of cancer, both the frequency and incidence rate of RRD are anticipated to increase in the future. Here we present a rare case of atypical docetaxel-induced RRD and compare it with the findings of previous studies.Radiation recall phenomenon (RRP) is an acute inflammatory or tissue reaction that appears in a previously irradiated area and is induced by the administration of certain triggering drugs, such as chemotherapeutic agents. Radiation recall dermatitis (RRD) associated with actinomycin D was first described by D’Angio et al22) every 5 weeks for hormone-resistant multiple metastases. After one cycle of docetaxel, radiotherapy for the thoracic and lumbar vertebrae was performed for back pain and lumbago. In addition, irradiation was performed using 10-MV photon beams (anterior) and 10-MV (posterior) photon beams, 30 Gy in 10 fractions, and the anterior:posterior dose weight was approximately 1:2 . Using 10-MV photons, he received 74 Gy in 37 fractions, 7 portals, and all coplanar irradiation. Although he experienced cystitis grade 1 (Common Terminology Criteria for Adverse Events 4.0.) as an adverse event, we observed no other adverse event. He continued receiving hormonal therapy for 2 years because of being in a high-risk group and became prostate-specific antigen (PSA) nadir. Six months since the completion of hormonal therapy, his PSA level increased again. Despite receiving hormonal therapy again, his PSA level continued increasing gradually. After 10 months of restarting hormonal therapy, he presented with dry cough and dyspnea. Based on the radiological examination, he was diagnosed with multiple lung, bone, and lymph node metastases. To distinguish primary lung cancer, biopsy was made from lung disease by bronchoscopy and established metastasis from prostate cancer. Accordingly, we started docetaxel , only the area treated with 40 Gy developed RRD. Regarding these studies, the administered radiation dose seems to play a crucial role in the development of RRD. Perhaps, a comparatively lower dose might not induce RRD or cause a mild reaction. However, our case reported an atypical appearance at this hypothesized radiation dose. Our radiotherapy consisted of 2 anterior–posterior portals, and the posterior portal had approximately 2 times the dose weight compared with that of the anterior portal; the radiation dose of the skin was higher in the posterior skin than in the anterior skin. However, RRD was more severe on the anterior skin than on the posterior skin, in particular, the posterior skin of the thoracic part did not develop RRD. Sears  reported hydroxyurea-induced RRD in 2 treated fields, but it was observed 8 days earlier in a field that had received a higher dose. In our case, we continued the follow-up for a while, but did not detect RRD on the posterior thoracic part. In our experience, RRD might appear despite low skin dose, unlike that reported previously. No RRD developed irrespective of a high radiation dose (posterior thoracic part) in our patient, raising skepticism on the correlation between a dose of radioactivity and its appearance.Mostly previous studies have reported that RRD occurs with lower energy photon beams,,12,16 whereas RRP occurred within 7 days in our patient, as reported previously. The longest interval between radiotherapy and RRD is 7 years. Our patient received radiotherapy for primary prostate cancer 2 years ago, when RRD could also have developed in the lower pelvic site of the skin but did not develop. Radiation dose is similar to that during radiotherapy for bone metastases, RRD developed at this radiotherapy for bone metastases but did not develop during the previous radiotherapy for prostate cancer. Although the mechanism is unknown, we hypothesize that the skin surface that receives radiation once, recovers with time, and it becomes difficult of RRD to occur at the same dose.RRP, including dermatitis and stomatitis, indicates that this phenomenon tends to develop approximately 1 week after the administration of triggering agents, reported a case in which gemcitabine was administered without a effect on the skin on day 1, but RRD was reported after the day 8 dose. In our case, the first administration of docetaxel at 18-Gy radiation schedule reported no adverse skin reaction. These results support the presence of RRD.Usually, RRD occurs after the first exposure to a recall-triggering drug. In contrast, Castellano et al thereby necessitating further investigation.In our case, successful re-administration of docetaxel without recurrence of RRD occurred without reducing the docetaxel dose and not using a steroid. Regarding the re-administration of docetaxel after the appearance of RRD once, a dose reduction of the trigger drugs, using oral steroids, or re-challenging the administration on the same schedule have been reported, but their efficacy remains unproven,4Although several hypotheses on drug hypersensitivity reactions, with or without additional radiation-induced cellular sensitivity to such reactions, have been described, its underlying mechanism remains unclear. In our case, the atypical feature was different from that reported previously. This case report draws attention to the development of atypical RRD that warrants further investigation in the future.Conceptualization: Takuya Aizawa, Naoya Ishibashi, Masakuni Sakaguchi.Data curation: Masakuni Sakaguchi.Methodology: Masakuni Sakaguchi.Supervision: Toshiya Maebayashi.Writing – original draft: Masakuni Sakaguchi.Writing – review & editing: Masakuni Sakaguchi, Toshiya Maebayashi, Takuya Aizawa, Naoya Ishibashi.Masakuni Sakaguchi: 0000-0002-7204-0701.


The Hippo signaling pathway is a highly conserved pathway controlling organ size, cell proliferation, apoptosis and other biological functions. Recent studies have shown that Hippo signaling pathway also plays important roles in cancer initiation and progression. However, a database offering multi-omics analyses and visualization of Hippo pathway genes in cancer, as well as comprehensive Hippo regulatory relationships is still lacking. To fill this gap, we constructed the Regulation of the Hippo Pathway in Cancer Genome (RHPCG) database. Currently, RHPCG focuses on analyzing the 21 core Hippo-protein-encoding genes in over 10 000 patients across 33 TCGA (The Cancer Genome Atlas) cancer types at the levels of genomic, epigenomic and transcriptomic landscape. Concurrently, RHPCG provides in its motif section 11 regulatory motif types associated with 21 core Hippo pathway genes containing 180 miRNAs, 6182 lncRNAs, 728 circRNAs and 335 protein coding genes. Thus, RHPCG is a powerful tool that could help researchers understand gene alterations and regulatory mechanisms in the Hippo signaling pathway in cancer. The Hippo signaling pathway is a highly conserved pathway initially discovered in Drosophila  and consYAP1 and its paralog TAZ have been reported to function as oncogenes in various malignant tumors. Specifically, YAP1/TAZ have been shown to play the role of transcriptional coactivators with the TEA-domain DNA-binding family of transcription factors (TEADs) during carcinogenesis, including cell proliferation, epithelial-mesenchymal transition (EMT) and apoptosis. Overexpression and activation of YAP1 has been involved in the development of gastric cancer (SAV1) is the human homologue of the Salvador protein and was reported to repress human colorectal cancer by inhibiting the Akt-mTOR signaling pathway  as an open and up-to-date online manually curated depository of molecular alterations and interactions of the Hippo-signaling-pathway genes. The RHPCG database collected six types of regulatory ships among protein coding genes, microRNAs, lncRNAs and circRNAs from 12 public databases and integrated them into 11 diverse motifs. In addition, RHPCG systematically analyzed the multi-omics data obtained from The Cancer Genome Atlas (TCGA) containing genomic, proteomic, epigenomic and transcriptome data. Meanwhile, the RHPCG database provides a user-friendly web interface that allows users to browse, search and download the gene alterations of Hippo signaling pathway in pan-cancer at multiple levels of omics, as well as the regulatory motifs associated with Hippo signaling pathway.Here, we developed a database, named Homo sapiens were collected from the NCBI database (HUGO Gene Nomenclature Committee (HGNC) guidelines and Hippo-signaling-pathway genes were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG).Protein-coding genes of database . Likewisdatabase . Mature database . The gendatabase . All genhttp://gdac.broadinstitute.org/), containing over 10 000 samples and 33 cancer types . Genes with interactions with the Hippo-signaling-pathway-core genes constituted the potential targets. For the target gene differential expression test, we used the t-test to evaluate whether potential targets were differentially expressed between samples of the high-expression group and low-expression group divided by the mid-value of mRNA expression of Hippo core gene based on TCGA cancer expression profiles.We downloaded the gene-interaction relationship dataset from Pathway Commons  architecture were implemented to build the dynamic database backend. The RHPCG database was built on an Apache HTTP server (version 2.4.6) installed on a machine with CentOS (version 7.2.1511) as the operating system. All data in RHPCG had been organized using MySQL (5.6.34). The website was developed based on Django and HTML. Web components were designed in Cascading Styling Sheet (CSS) and Javascript (JS), and tested in the latest versions of Google Chrome (version 76.0.3809.100) and Firefox (version 68.0.1) browsers.NF2), WW and C2 domain containing 1 (WWC1), FERM domain containing 6 (FRMD6)),  and (YAP1/TAZ)  . The memtivators . In contferation . Based oferation .In its current version, RHPCG has collected data from over 10 000 patients across 33 TCGA cancer types at the levels of genomic, epigenomic and transcriptomic landscape . MeanwhiThe RHPCG database provides a user-friendly web interface that allows user to browse, search, and download alterations of Hippo-signaling-pathway genes in pan-cancer at multiple levels of omics, as well as the regulatory motifs associated with the Hippo signaling pathway.The RHPCG interface has two distinct searching sections, either searching by genes or by cancer type. On the top of the ‘searching by gene’ page, the multi-omics overview section provides an overview of the alterations of Hippo core genes across 33 cancer types at different levels of omics . At the In a similar manner, search results were divided into three main parts: The first part is the information box. The box contains the gene symbol, Entrez ID with link to source website, the location in the chromosome and a brief description of the function of the gene . In the http://www.pathwaycommons.org)  . We usedre genes . Obtaineificance .In the ‘Download’ page, users can download multi-omics datasets of Hippo-signaling-pathway genes, direct target interactions and all information on the 11 motif types. Users can conveniently access the link by clicking on the navigation panel on the right .We integrated all interactions of TF-target, miRNA-target, miRNA-lncRNA, TF-lncRNA, TF-miRNA and miRNA-circRNA as a huge regulatory network.AMOT) and caudal type homeobox 2 (CDX2) were the hubs in the FFLs formed subnetwork  , whereas AMOT was significantly downregulated in HNSC  . Thus, we inferred that hsa-miR-205-5p and CDX2 coordinately regulate AMOT in the development of HNSC, which warrants for further biological experiments required for validation.Then, we searched for 11 types of motifs in the integrated regulatory network. The details of these motifs are given in bnetwork . In part(3′-UTR) . Accordi pathway . The CDXR-205-5p . We then t-test) . More spYAP1/TAZ coregulators, which in turn affect downstream transcription processes, such as EMT and cell differentiation. Many studies have explored the regulatory patterns and molecular changes of the Hippo signaling pathway in cancer. Motifs, such as FFLs and ceRNAs, have been shown to be the basic patterns in the regulatory networks of tumors. To facilitate researchers in exploring the regulatory mechanisms of genes in the Hippo pathway and to further reveal the molecular alterations in multi-omic levels of the Hippo pathway genes, we integrated data from 12 public databases and analyzed TCGA multi-omics datasets to construct the RHPCG database.It is well known that the Hippo signaling pathway plays important roles in tumor initiation and development. Through a series of kinase cascades, the Hippo signaling pathway activates the Dysregulation of the Hippo signaling pathway was found in several types of cancers, suggesting that the Hippo signaling pathway might constitute an effective target for cancer treatment . Most stIn conclusion, the RHPCG database can serve as a valuable resource for understanding the roles of Hippo signaling in carcinogenesis. The features of RHPCG can offer: (i) an overall view of the Hippo core genes in multiple omics; (ii) a quick search of Hippo regulatory motifs by mRNA, lncRNA, circRNA or miRNA; and (iii) visualization of all the results of Hippo-related motifs as a network that can be downloaded freely. In the future, we plan to add more datasets and investigate the cross talk with other tumor-related signaling pathways. We believe that RHPCG can help researchers gain an improved perspective of the alterations in the Hippo signaling pathway, as well as assist them in more efficiently filtering for the respective regulatory motifs. Moreover, we believe that RHPCG could also facilitate future studies in further exploring the biological molecular mechanisms in cancer. Thus, the RHPCG database could offer a new way in explaining how these motifs regulate the Hippo signaling pathway in cancer.Supplementary_table20191018_baz135Click here for additional data file.