Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells.DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE).In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms.Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes  for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring  contains supplementary material, which is available to authorized users. Determining the responses of individual patients to drugs has become a critical task in the practice of personalized medicine. Experimental efforts have been undertaken to directly measure drug response of the cells extracted from patients’ cancerous tissues, including in-vitro and in-vivo models . While sWith the advance of high-throughput genomic technologies, pharmacogenomics is becoming a powerful approach to determine individuals’ responses to drug therapies . TypicalExploring these data-resources can help uncover new drug mechanisms and further personalize drug therapies. Currently, most of the computational models to predict drug sensitivity of cancer cell lines involve gene-level features like gene expression . HoweverIn this study, we investigate four representative approaches to score pathway activities solely based on gene expression data alone. Specifically, these four approaches were compared based on 24 drugs from CCLE dataset  models t50) were collected from the CCLE for 24 drugs. Specifically, raw gene expression data (Affymetrix cel files) was first extracted and normalized with Bioconductor Affy package  and then log-transformed. For genes with multiple probesets, the optimal probeset was then determined using R package jetset  was searched in cross validations.Once pathway activity scores are generated for cell lines, various machine learning models could be applied to predict drug response. We noticed that most individual pathway-level or gene-level features were modestly correlated to drug response for most drugs (data not shown). For such datasets, machine learning models with regularization (i.e. Elastic net) have proven promising to achieve better predictions, as demonstrated by model choices in previous studies , 8 and tThe overlaps of member genes were first explored for all 1410 pathways. Specifically, Jaccard Index was calculated to measure the overlap between two pathways. The value of Jaccard index ranges from zero (for pathways without overlapping genes) to one . Figure p-value would be zero if all random MACCs are smaller than real MACC. The results  was taken as an overall measure of pathway member gene correlations. Then permutation test was performed to determine if member genes within one pathway have higher correlations than by chance. Specifically, gene expressions of cell lines were randomly shuffled for 1000 times to generate a vector of random median correlations (MACC). For each pathway, the statistical significance of real MACC is then determined by comparing to random MACCs. For example, the PLAGE, Z-Score, GSVA and DiffRank. All four pathway score metrics were first calculated for all cell lines with gene expression data in CCLE. For each drug, cell lines with both response data (IC50) and pathway activity scores were used to build the prediction models. In particular, elastic net model was trained for pathway activity scores with 10-fold cross validation to determine optimal parameters yielding minimal mean square error (MSE). As a comparison, elastic net models were also trained with gene level expression data for all 24 drugs. Figure Pathway approaches have been applied to identify disease biomarkers and patient stratification. Critical to these approaches is to characterize pathway activity with a quantitative score aggregated from gene expression data. Four unsupervised pathway scoring approaches are compared in our study, including DiffRank performs the best for 9 drugs and the second best for 8 drugs, whereas GSVA has best prediction for 7 drugs and the second best prediction for 5 drugs. Z-Score and PLAGE have best prediction performance for the rest 8 drugs, but poorest performance for 16 drugs. The superior performance of competitive scoring over self-contained score suggested that incorporating both member and non-member genes may better capture the variations of pathway activities among individual samples. Comparing to gene-level models, at least one pathway-based model perform better for 14 of the 24 drugs. Take DiffRank as an example, it outperforms gene-level models for 11 drugs. Meanwhile, gene-level models perform the poorest for three drugs . For these drugs, pathway-based models could be promising alternatives for predicting their sensitivity on cancer cells.As shown in Fig. Elastic net identifies the features with non-zero weights as important features predictive of cellular response to drugs. In order to evaluate the biological relevance of important features identified from elastic net models, we have collected the drug-related genes  from commercial and public resources  for all drugs  tend to identify drug-related pathways for more drugs than self-contained approaches (Z-score and PLAGE). However, none of these approaches identify drug-related pathways for three drugs, including PD-0332991,TAE684 and TKI258.Figure We also looked into the genes identified by the gene-level models described earlier, against the drug-related genes. It turns out that these gene-level models identified only one target gene for three drugs , one enzyme gene for Sorafenib, but could not capture any drug-related genes for all the other 15 drugs. This indicates gene expression alone can barely identify drug-related genes in majority of cases, which corroborates the notion that the activities of many targeted proteins are not necessarily reflected by their gene expressions.DiffRank tends to achieve better predictions for more drugs and is more capable of capturing pathways involving drug-related genes. For the 24 CCLE drugs, DiffRank performed either best or second best for 17 drugs, and identified pathways with drug-related genes for 18 drugs, with 14 drugs in common. We then investigated biological relevance of important pathways (with non-zero coefficients in models) identified by DiffRank for these 14 drugs. In Fig. Blue for pathways with target genes and at least one transporter or metabolic enzyme; (2) Red for pathways involving target genes only; (3) Orange for pathways involving metabolic enzymes only; (4) Green for pathways involving transporter genes only. Please see Additional file Among the four pathway scoring approaches, es only;  Orange fDiffRank identified top pathways involving drug-related genes (particularly drug targets) for several drugs, including 17-AAG, AEW541, Irinotecan, Topotecan, Lapatinib, Sorafenib, Paclitaxel and ZD6474. Because of space limitation, we would not discuss each pathway, but rather summarize and highlight a few advantages of pathway models with concrete examples. First, pathway models could identify pathways involving multiple targets. Taking Lapatinib as an example, this drug is a dual inhibitor of EGFR and ERBB2 (or HER2) [As demonstrated, or HER2) , and wasWe also found that pathway models could capture simultaneously both targets and biomarkers. For instance, the pathway “Mitomycin action” is ranked at top one for 17-AAG . Pathway models identified one common pathway “Cell Cycle- Chromosome Condensation” involving TOP1 for both drugs. Specifically, this pathway ranked 2nd for Topotecan and 6th for Irinotecan . Researcher has found that activation of TGF-beta receptor signaling confers to the resistance to PF2341066 . InteresIn this study, we evaluated different unsupervised pathway activity inference approaches for predicting drug sensitivity of cancer cell lines. Our study highlighted the ability of pathway-based models to reveal drug mechanisms, along with prediction performance comparable to gene-based models. Also, pathway-based approach could help generate testable hypotheses by looking at the difference of pathway activity scores between sensitive and resistance cell lines, as demonstrated by the cases in Fig. DiffRank and GSVA generally perform better than PLAGE and Z-Score. This suggests that incorporating expression of non-member genes could help better characterize pathway activities than approaches using member genes alone. In addition, both DiffRank and GSVA adopt a ranking-based strategy to calculate pathway activity for individual samples. Such ranking-based pathway activity is computable for single sample with gene expression profile, which makes it very straightforward to perform prediction on new samples, i.e. the N-of-1 situations in precision medicine. However, other approaches to compute pathway activity could be used as well. For example, pathways topology have been used to improve pathway enrichment analysis [A crucial step in pathway-based modelling is to convert gene expression profile to pathway activity scores for individual samples. Our analysis showed that analysis . In our In this study, Elastic net was used to build the predictive models of drug response. We recognize that other machine learning algorithms  could also be tested in an attempt to improve the prediction of some of the drugs that display poor correlations with IC50 values (data not shown). Prediction performance could also be improved by including additional -omics data types, such as copy number, methylation, etc. Finally, this study was based on canonical pathways, which involve only genes curated in pathway databases. More gene-sets could be assembled or derived from molecular interaction network, such as densely connected sub-networks or downstream target genes of regulators (i.e. transcription factors) etc. Such molecular networks could cover more genes that are involved in drug responses to improve the accuracy of the predictive models.We developed a pathway-based modelling strategy to predict drug response of cancer cells. The results show that pathway-based models achieve comparable or even better drug response prediction than gene-based models. Moreover, we have shown that pathway-based models recapitulate known drug response mechanisms for majority of drugs. Pathway-based models could serve as an effective alternative to gene-based models for predicting drug sensitivities of cancer cells.Additional file 1:Metrics of pathway member genes and their correlations. (XLSX 127 kb)Additional file 2:Target genes of CCLE drugs. (XLSX 16 kb)Additional file 3:Coefficients of pathways in elastic net models (based on DiffRank) for each CCLE drug. Pathways are indicated when their member genes are related to CCLE drugs. (XLSX 64 kb)Additional file 4:Coefficients of genes in elastic net models for two drugs. (XLSX 13 kb)Additional file 5:Pathway map for anti-apoptotic action of ErbB2 in breast cancer. (PNG 1088 kb)Additional file 6:Pathway map for mytomycin action. (PNG 853 kb)


Osteoclast differentiation in the inflamed synovium of rheumatoid arthritis (RA) affected joints leads to the formation of bone lesions. Reconstruction and analysis of protein interaction networks underlying specific disease phenotypes are essential for designing therapeutic interventions. In this study, we have created a network that captures signal flow leading to osteoclast differentiation. Based on transcriptome analysis, we have indicated the potential mechanisms responsible for the phenotype in the RA affected synovium.We collected information on gene expression, pathways and protein interactions related to RA from literature and databases namely Gene Expression Omnibus, Kyoto Encyclopedia of Genes and Genomes pathway and STRING. Based on these information, we created a network for the differentiation of osteoclasts. We identified the differentially regulated network genes and reported the signaling that are responsible for the process in the RA affected synovium.Our network reveals the mechanisms underlying the activation of the neutrophil cytosolic factor complex in connection to osteoclastogenesis in RA. Additionally, the study reports the predominance of the canonical pathway of NF-κB activation in the diseased synovium. The network also confirms that the upregulation of T cell receptor signaling and downregulation of transforming growth factor beta signaling pathway favor osteoclastogenesis in RA. To the best of our knowledge, this is the first comprehensive protein–protein interaction network describing RA driven osteoclastogenesis in the synovium.This study provides information that can be used to build models of the signal flow involved in the process of osteoclast differentiation. The models can further be used to design therapies to ameliorate bone destruction in the RA affected joints. Rheumatoid arthritis (RA) is a systemic autoimmune disease that primarily affects synovial joints. The disease is characterized by chronic inflammation in the joints, leading to synovial hyperplasia (pannus formation), destruction of the cartilage and erosion of the underlying bone. RA is a complex disease involving several molecular pathways across various cell types and tissues. Thus in order to elucidate the underlying cause of a particular phenotype associated to the disease, identification of the network consisting of differentially expressed genes (DEGs) in the interacting pathways is essential. Studies have used pathway analysis to identify affected pathways from lists of DEGs . The lisThis study involved two major steps: selection of a phenotype exhibited by the RA synovium, and construction and analysis of a PPI network for the selected phenotype. The DEGs were obtained by re-analyzing the publicly available microarray datasets in Gene Expression Omnibus (GEO)  databaseGSE77298  (GSE12021 (U133B)  , GSE1202 (U133B) , GSE5523GSE55235  and GSE5GSE55457 . The erypatients . For theRaw data from the seven datasets along with their metadata was downloaded using the R libraries GEOquery  and GEOmt test was applied to calculate the significance for differential expression between the RA and the control samples. As per the recommendations by p value ≤ 0.05 was considered to be differentially expressed. A final list of DEGs, from the seven datasets, was obtained using the selection rules as described below:It is upregulated in both RMA and MAS5 Or,It is upregulated in one of the algorithm and not differentially expressed in the other  or .In a dataset, a gene is considered to be upregulated if:It is  in at least one of the seven datasets and no downregulation in any of the datasetsOr,It is , in at least one dataset and  in at least one of the remaining datasets while there is no downregulation in any one of them.Across the datasets, the gene is upregulated if:In this analysis, two algorithms, Robust Multi-array Average (RMA) and Microarray Suite 5.0 (MAS5) were used for the data normalization. The choice of data normalization algorithms affects the final selection of the DEGs . In ordeThe same procedure was repeated for the downregulated genes.When the selection criteria was made more stringent by demanding the selection of a gene in at least two datasets, the number of selected genes reduced by almost 50% as shown in Finally, we prepared a list of up and downregulated genes which we named as “common-up” and “common-down,” respectively.The common-up and common-down gene lists were separately examined for the enrichment of pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  using DAThe pathways were grouped according to their KEGG categories. Those belonging to the categories “human diseases,” “metabolism” or the ones lacking PPIs were not considered further. The category “human diseases” contains pathways that represent specific disease conditions. These were excluded because they do not reflect the PPI in healthy conditions. “Metabolism” contains pathways that describe interconversions of metabolites. Since this study focused on PPI involved in the RA affected synovium, we did not consider the metabolic pathways. The remaining pathways were categorized based on their functional specificity. The pathways which result in a specific function like platelet activation were tagged as process pathways, whereas the pathways describing more general signaling events like the activation of multiple transcription factors (TFs) through T cell receptor signaling, were considered as signaling pathways.The list of genes present in each selected pathway was downloaded from KEGG using the KEGGREST  package We constructed a PPI network for the osteoclast differentiation. The osteoclast differentiation pathway (ODP) proteins, obtained from KEGG were defined as a set of core proteins. Interactors of the core proteins (first-shell interactors) were extracted from the STRING database (version 10) . The proThe network was a mixed network consisting of the undirected protein binding edges and the directed edges of activation, inhibition or the post-translational modification (PTM). The nodes are labeled using the official gene symbols corresponding to the proteins used to create the network. The interactions involved undirected PPIs or directional PTM like phosphorylation, methylation, acetylation, ubiquitination, etc. We included activation or inhibition as an interaction whenever the reference mentioned that the target protein is activated or inhibited as a result of the interaction. We created a version of this directed network without ubiquitin C and its edges. We named this version as “directed ODP network.”We conducted GO enrichment analysis on the directed ODP network proteins using the GO molecular function (GOMF) and GO biological process (GOBP) terms. Terms with an EASE score ≤0.05 and fold enrichment ≥1.5 were considered as enriched. We combined 23 enriched GOMF terms to identify the proteins that bind to DNA. In the case of the GOBP terms, we selected the enriched signaling terms that contained differentially regulated genes for T cell receptor signaling, B cell receptor signaling and FC-ε receptor signaling. For NF-κB signaling, toll-like receptor (TLR) signaling and TGFβ signaling pathways we combined six, nine and three enriched terms, respectively, and examined their differential regulation. Using the selected enriched signaling terms, we extracted subnetworks corresponding to each signaling pathway from the directed ODP network. All the subnetworks demonstrate the flow of information from the first-shell interactor proteins to the core proteins of the ODP network. The details of the GOMF and GOBP signaling terms enriched in the analysis is provided in the The database DrugBank  was explWe converted all the edges of the directed ODP network to single undirected protein binding edges to create an “undirected ODP network.” We analyzed this network using the Cytoscape plugin NetworkAnalyzer . We usedWe analyzed seven Affymetrix microarray datasets from five different platforms. Out of 21,246 Entrez annotated genes measured, 1,018 upregulated and 893 downregulated genes were identified in the RA synovium compared to the healthy controls. The differentially regulated genes are submitted in Using the DEGs from the microarray analysis as the input, we found that 52 KEGG pathways were enriched in the upregulated gene list, and 29 in the downregulated gene list. The EASE scores of the selected pathways were much less that the cut-off of 0.05. The enrichment analysis when performed with the combined DEG list  resulted in only 55 pathways. Among these, only two pathways were newly obtained when compared to the previous list of 52 upregulated and 29 downregulated pathways. As the combined analysis proved less informative, the 52 up and 29 downregulated pathways were considered for the study. Three pathways, namely, extra cellular matrix-receptor interaction, Focal adhesion and Proteoglycans in cancer occurred in both the up and downregulated pathway lists because each pathway had a significant number of up and downregulated genes. The KEGG category-wise distributions of the enriched pathways are shown in Among the upregulated non-disease pathways, the category “immune system” had the highest number of enriched pathways. Most of the immune receptor signaling pathways in this category were upregulated. Among the other signaling pathways, NF-κB and JAK-STAT signaling pathways were upregulated in our analysis. All the signaling pathways which belong to the KEGG categories, “immune system” and “signal transduction” that were enriched in the upregulated gene list are shown in GSE1919, GSE12021 (U133A), GSE12021 (U133B), GSE55235 and GSE55457, used tissue from patients with more than 10 years of disease. Patients from the other dataset GSE77298, were at the end stage of the disease. Since osteoclast differentiation is reported in severely inflamed RA synovium, the process is likely to be detected in the synovial tissue used for the datasets. It is noteworthy that our analysis identified the pathway osteoclast differentiation as one of the enriched pathways with a fold enrichment of 3.11 (EASE score of 2.41 e−8) in the RA synovium. In addition, our analysis detected the upregulation of two well-known osteoclast markers Cathepsin K and tartrate resistant acid phosphatase 5 (ACP5) in the synovium.Specialized cells called osteoclasts which facilitate bone resorption are also present in the invading pannus of the RA joints . All theThe category highly represented in the list of downregulated pathways was “signal transduction.” All the signal transduction pathways enriched in the downregulated gene list are given in We categorized the 26 upregulated and 25 downregulated non-disease pathways based on their functional specificity. In this analysis, we identified 12 process pathways and 19 signaling pathways among the differentially regulated pathways. This list of 31 pathways includes the process pathway focal adhesion which was differentially regulated in both directions. While 4 and 10 process and signaling pathways, respectively, were downregulated, 9 process and signaling pathways each were upregulated. The detailed information about the number of upregulated, downregulated and total genes in each of the selected pathway is submitted as We examined the overlap of the process pathways with all the non-disease pathways based on the shared number of DEGs. The overlapping pathways are represented as a pathway interaction network in Among the downregulated process pathways shown in Our study revealed that the processes of natural killer cell mediated cytotoxicity as well as osteoclast differentiation involved a network of several interacting pathways in the RA synovium. However, osteoclast differentiation was influenced by the highest number of signaling pathways. This indicates that the differentiation of osteoclasts in the RA synovium is coordinated by several signaling pathways. In order to understand the collective effect of these signaling pathways on the osteoclast differentiation, we created a detailed PPI network for the process in the RA synovium. Among all the interactions obtained from the STRING database, we used only the experimentally validated ones published in literature, for the creation of the network. In this network, we indicated the differentially regulated genes from the microarray analysis to show the possible ways by which the altered signaling promotes osteoclastogenesis in RA synovium.The PPI network, created in our study, had 433 proteins and 1,790 interactions. The network consisted of three connected components. The two smaller connected components were the interactions between CD47 and signal regulatory protein alpha (SIRPA), and IL1A and S100A13.The protein ubiquitin which has the highest number of interactions in the network was found to interact with 175 network proteins. This is expected, as ubiquitination is a very common PTM that marks the proteins for proteasomal degradation. In our network, ubiquitination was represented as interaction of a protein with ubiquitin as well as with ubiquitin ligases. We removed ubiquitin from our network since most of the edges of ubiquitin and those of the ubiquitin ligases were redundant. The resulting network had 432 proteins and 1,595 interactions. In this network, in addition to the two small connected components, four proteins, PPP3R1, PPP3CA, PPIA and regulator of calcineurin 1 (RCAN1) were disconnected from the main network and formed a new connected component. The network now had four components: CD47-SIRPA, IL1A-S100A13, PPP3R1-PPP3CA-PPIA-RCAN1, and one large component.core network” (directed). The rest of the network consisting of the first-shell interactors and their edges is the “shell network” (directed). The core network contains proteins which are directly involved in the osteoclast differentiation. The shell network represents the protein milieu in the RA synovium facilitating the osteoclast differentiation. The complete directed ODP network is provided as a We removed the three smaller connected components from the main network. The large connected component, consisting of 424 protein nodes and 1,589 interactions, was used for further analysis. Henceforth, we refer to this as directed ODP network. The directed ODP network contains 82 core proteins belonging to the KEGG ODP. The portion of the directed ODP network containing the 82 core proteins and their 152 connections is termed as the “In a PPI network, the terminal responders of the signals are the DNA-binding proteins such as a TFs, coactivator, etc., or the proteins that generate non-protein signaling molecules like secondary messengers. Using the enriched terms in the category GOMF, we classified 82 of the 424 nodes as DNA-binding proteins . A totalIn addition to STAT and AP1 proteins, the other DNA binding core protein peroxisome proliferator activated receptor gamma (PPARG) was observed to be downregulated which is in agreement with the results of GO biological process over-representation analysis of the directed ODP network proteins identified several immune signaling terms. These terms included five out of the eight upregulated KEGG signaling pathways which were found to interact with the osteoclast differentiation . These pAmong these, T cell receptor signaling pathway had the most number of DEGs, with 14 up and one downregulated nodes. The proteins belonging to the T cell receptor signaling term were extracted as a subnetwork . In thisThe B cell receptor signaling pathway  term shaThe Fc-ε receptor signaling pathway  showed tSix GOBP terms were combined to extract 75 proteins of the NF-κB signaling pathway from the directed ODP network . This NFWe combined nine over-represented GOBP terms to extract the TLR signaling pathway proteins . The extInterestingly, the directed ODP network demonstrated a downregulation of all the DEGs participating in the TGFβ signaling pathway. The TGFβ subnetwork  showed tThe analysis done using the MCODE application of the Cytoscape tool revealed 19 clusters which may indicate functional protein complexes. The details of these 19 clusters are submitted as This study is aimed at understanding the mechanisms involved in a specific RA-related phenotype. We have used a large number of microarray studies and relaxed inclusion criteria for differential expression across datasets, to obtain relatively large number of DEGs that are likely to be involved in RA. We have combined this gene expression data with pathway analysis and identified various process pathways and several signaling pathways to be affected by RA. In systemic diseases like RA, pathways responsible for a particular phenotype operate in an environment consisting of various other disrupted pathways. Thus it becomes important to understand the effect of this environment on the pathway immediately responsible for the phenotype. We attempted to achieve this by overlapping the various process pathways with the enriched signaling pathways in the synovium. Interestingly, the process pathway osteoclast differentiation overlapped with several of the enriched signaling pathways. In order to understand the signaling involved in osteoclast differentiation in the RA synovium, for the first time, we created a detailed PPI network responsible for the phenomenon. Each interaction in this network was manually verified from literature enabling the inclusion of directions of the interactions and specific PTMs whenever such information was available. While creating the network using all the possible interactions available in STRING, we found that some proteins in the repository have more number of interactions reported than the others. We acknowledge that this might have led to a bias in the directed ODP network. The network lacks the important non-protein molecules involved in triggering the ectopic differentiation of osteoclasts in the inflamed synovium. In addition, gene expression regulation resulting from activation or repression of TFs was not depicted in the network. Since the RA specific data used in this network was only gene expression data, information on the activation state of specific proteins that are known to be involved in the disease, for example, phosphorylation state of STAT1 was missing. Though the network lacks these information, it is the most comprehensive and informative PPI network till date describing the process of osteoclast differentiation.GSE1919, GSE12021 (U133A), GSE12021 (U133B), GS55235 and GSE55457 had similar high values for the inflammatory markers, ESR and CRP. Additionally, the tissue used in GSE77298 were described as end stage RA synovial biopsies. Therefore, we surmise that the RA tissues were highly inflamed. However, we observed that few genes were differentially expressed across most of the datasets  resulted in 26 upregulated disease pathways. As expected, RA was one of these disease pathways. Staphylococcus aureus infection and Tuberculosis, the two upregulated infectious disease pathways in the results of the pathway enrichment analysis are known to be associated with RA . The uprSeveral pathways belonging to the category “immune system” were enriched in the common-up genes. The enrichment of these immune pathways is likely as infiltration of activated immune cells has been observed in the RA synovium . In addiAmong the enriched downregulated pathways, seven belong to the category “signal transduction” . The AMPThree small connected components were removed while creating the directed ODP network. One of the components contained the proteins PPP3R1 and PPP3CA which are the subunits of calcineurin, an important regulator of osteoclast differentiation. The immunosuppressant drug cyclosporin used in the treatment of RA acts as an inhibitor of calcineurin by forming a ternary complex with PPIA and calcineurin . The remThe directed ODP network was analyzed to examine three main aspects. Firstly, the proteins binding to DNA were explored because they are involved in regulating gene expression. Secondly, the proteins involved in signal transduction were studied for their role in facilitating the process of osteoclast differentiation in the RA affected synovium. Finally, the clusters of highly interconnected proteins were identified in the network. Several proteins function as part of protein complexes. The protein clusters we identified may represent the protein complexes. We also examined the differential expression of the proteins in the clusters. Protein clusters with differentially regulated proteins may represent complexes actively involved in the osteoclastogenesis in RA synovium.Among the DNA-binding proteins in the directed ODP network, the downregulation of the AP1 proteins JUN and JUNB is in contrast to the earlier studies which reported their upregulation in RA synovium . As showAccording to previous reports, another AP1 protein FOS is upregulated in the RA synovium . HoweverThe examination of the enriched GOBP terms of the network showed an involvement of T cell receptor signaling pathway, B cell receptor signaling pathway, Fc-ε receptor signaling pathway and NF-κB signaling pathway in the differentiation of osteoclasts in the RA synovium. The GOBP analysis revealed enrichment of TGFβ signaling pathway terms. The role of TGFβ signaling pathway in osteoclastogenesis is not captured in the KEGG ODP as it shows only the receptor-ligand interaction. Our network connects the TGFβ receptors to downstream DNA-binding proteins, of which Forkhead box proteins, SMAD3 and ATF3 were downregulated. The consistent downregulation of all the DEGs in the TGFβ pathway implies their negative regulatory role in the osteoclastogenesis. Although the published studies support a mixed role of TGFβ pathway in the RA synovium, It is known that the NADPH oxidase 2 (Nox2) complex generates ROS which act as secondary messengers during osteoclast differentiation . ROS areIn this study, we have created a PPI network for osteoclast differentiation in the RA synovium for the first time using gene expression under RA conditions from microarray experiments, pathway enrichment analysis and PPI data. This network captures all the signaling routes that lead to osteoclastogenesis in the synovium and depicts the roles of T cell receptor signaling, canonical NF-κB pathway and ROS generation.10.7717/peerj.5743/supp-1Supplemental Information 1The highlighted GOMF terms were combined to identify the proteins that bind to DNA. In this table, each row signifies a particular GOMF term. The GO term name along with the GO term identifier is listed in column B. The “Count” in column C denotes the number of genes corresponding to the network proteins which belong to the GOMF term. The Entrez ID of these genes are provided in column D. The parameter in column E represents the percentage of network genes that are part of the GOMF term. “List Total” denotes the total number of network genes. “Population Total” represents the total number of genes that belong to the GOMF DIRECT category of the GO database. “Population Hits” signifies the number of the “Population Total” genes which are part of the GOMF term. The “Fold.Enrichment” column is the proportion of “Count”/“List Total” to “Population Hits”/ “Population Total.” The “PValue” is the EASE score which represents the significance of obtaining “count” genes in the “List Total” for the given GOMF term which has “Population Hits” genes in the background “Population Total.” The columns termed as “Bonferroni,” “Benjamini” and “FDR” represent the adjusted Pvalue to control the effect of multiple testing. The table also includes the information about the differentially regulated genes that belong to each of the GOMF term.Click here for additional data file.10.7717/peerj.5743/supp-2Supplemental Information 2The GOBP terms highlighted in green were combined to identify the proteins that belong to NF-κB signaling pathway. The GOBP terms highlighted in blue were combined to identify the proteins that belong to TGFβ signaling pathway. The GOBP terms highlighted in pink were combined to identify the proteins that belong to TLR signaling pathway. In this table, each row signifies a particular GOBP term. The GO term name along with the GO term identifier is listed in column B. The “Count” in column C denotes the number of genes corresponding to the network proteins which belong to the GOBP term. The Entrez ID of these genes are provided in column D. The parameter in column E represents the percentage of network genes that are part of the GOBP term. “List Total” denotes the total number of network genes. “Population Total” represents the total number of genes that belong to the GOBP DIRECT category of the GO database. “Population Hits” signifies the number of the “Population Total” genes which are part of the GOMF term. The “Fold.Enrichment” column is the proportion of “Count”/“List Total” to “Population Hits”/“Population Total.” The “PValue” is the EASE score which represents the significance of obtaining “count” genes in the “List Total” for the given GOBP term which has “Population Hits” genes in the background “Population Total.” The columns termed as “Bonferroni,” “Benjamini” and “FDR” represent the adjusted Pvalue to control the effect of multiple testing. The table also includes the information about the differentially regulated genes that belong to each of the GOBP term.Click here for additional data file.10.7717/peerj.5743/supp-3Supplemental Information 3The two biologics in the drug list, Etanercept and Denosumab are highlighted in blue. In this table, each row signifies an ODP network protein that is a target for a particular drug which is used in the treatment for RA as listed in the DrugBank database. The table includes the Entrez ID and name of the ODP network protein. It also includes the DrugBank accession number and the name of the drug used in the treatment for RA.Click here for additional data file.10.7717/peerj.5743/supp-4Supplemental Information 4The up and downregulated genes are listed in separate pages namely “UPREGULATED_GENES” and the “DOWNREGULATED_GENES” of the table. The table includes the Entrez ID, the name and official gene symbol for each of the DEGs.Click here for additional data file.10.7717/peerj.5743/supp-5Supplemental Information 5The KEGG pathway term name along with the KEGG pathway identifier is listed in column B. The “Count” in column C denotes the number of upregulated genes which belong to the KEGG pathway term. The Entrez ID of these upregulated genes are provided in column D. The parameter in column E represents the percentage of upregulated genes that are part of the KEGG pathway term. “List Total” denotes the total number of upregulated genes which are part of the KEGG pathway database. “Population Total” represents the total number of genes that belong to the KEGG Pathway database. “Population Hits” signifies the number of the “Population Total” genes which are part of the KEGG pathway term. The “Fold.Enrichment” column is the proportion of “Count”/“List Total” to “Population Hits”/“Population Total.” The “PValue” is the EASE score which represents the significance of obtaining “count” genes in the “List Total” for the given KEGG pathway term which has “Population Hits” genes in the background “Population Total.” The columns termed as “Bonferroni,” “Benjamini” and “FDR” represent the adjusted Pvalue to control the effect of multiple testing.Click here for additional data file.10.7717/peerj.5743/supp-6Supplemental Information 6The KEGG pathway term name along with the KEGG pathway identifier is listed in column B. The “Count” in column C denotes the number of downregulated genes which belong to the KEGG pathway term. The Entrez ID of these downregulated genes are provided in column D. The parameter in column E represents the percentage of downregulated genes that are part of the KEGG pathway term. “List Total” denotes the total number of downregulated genes which are part of the KEGG pathway database. “Population Total” represents the total number of genes that belong to the KEGG Pathway database. “Population Hits” signifies the number of the “Population Total” genes which are part of the KEGG pathway term. The “Fold.Enrichment” column is the proportion of “Count”/“List Total” to “Population Hits”/“Population Total.” The “PValue” is the EASE score which represents the significance of obtaining “count” genes in the “List Total” for the given KEGG pathway term which has “Population Hits” genes in the background “Population Total.” The columns termed as “Bonferroni,” “Benjamini” and “FDR” represent the adjusted Pvalue to control the effect of multiple testing.Click here for additional data file.10.7717/peerj.5743/supp-7Supplemental Information 7The process pathways are highlighted in blue and the signaling pathways are highlighted in pink. The pathways belonging to category of “metabolism” or the ones that lack protein-protein interactions in the KEGG database are white rows. The KEGG pathway identifier and the name are listed in column A and B respectively. Column C and D contain the information about the total number of genes that belong to each of the KEGG pathway term. Of the total genes, information about the ones that are assayed in the microarray data analysis is provided in column E and F. The information about the upregulated and downregulated genes in each pathway is provided in the columns G-J.Click here for additional data file.10.7717/peerj.5743/supp-8Supplemental Information 8The process pathways are highlighted in blue and the signaling pathways are highlighted in pink. The pathways belonging to category of “metabolism” or the ones that lack protein-protein interactions in the KEGG database are white rows. The KEGG pathway identifier and the name are listed in column A and B respectively. Column C and D contain the information about the total number of genes that belong to each of the KEGG pathway term. Of the total genes, information about the ones that are assayed in the microarray data analysis is provided in column E and F. The information about the upregulated and downregulated genes in each pathway is provided in the columns G-J.Click here for additional data file.10.7717/peerj.5743/supp-9Supplemental Information 9The table contains the information about the total number of genes and DEGs common between the pair of pathways. In this table, each row is meant for a pair of enriched pathway in the RA synovium. The KEGG pathway identifier and the name of each pathway in the pair are listed in the columns A-D. Columns E and F represent the Entrez ID and the total count of the genes that are common between the pair of pathways. Columns G-L signify the the Entrez ID and the total count of the DEGs that are common between the pair of pathways. The sum of all the genes belonging to each of the pathway pair is listed in column M of the table.Click here for additional data file.10.7717/peerj.5743/supp-10Supplemental Information 10The highlighted clusters contain DEGs in the core network. The number and the official gene symbols of the genes corresponding to the proteins belonging to the protein clusters are listed in the columns named “#nodes” and “nodes.” The number of interactions (edges) among the proteins in the clusters are included in the column named “# edges.” The information about the number and the official gene symbols of the DEGs present in the clusters are provided in columns E-L. Among these, the details of DEGs which are part of the core ODP network are listed in columns M-R.Click here for additional data file.10.7717/peerj.5743/supp-11Supplemental Information 11natural killer cell mediated cytotoxicity pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the natural killer cell mediated cytotoxicity pathway and each of the enriched non-disease pathways.The x-axis represents the enriched non-disease pathways with which the Click here for additional data file.10.7717/peerj.5743/supp-12Supplemental Information 12The x-axis represents the enriched non-disease pathways with which the focal adhesion pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the focal adhesion pathway and each of the enriched non-disease pathways.Click here for additional data file.10.7717/peerj.5743/supp-13Supplemental Information 13leukocyte transendothelial migration pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the leukocyte transendothelial migration and each of the enriched non-disease pathways.The x-axis represents the enriched non-disease pathways with which the Click here for additional data file.10.7717/peerj.5743/supp-14Supplemental Information 14Fc-γ receptor mediated phagocytosis pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the Fc-γ receptor mediated phagocytosis pathway and each of the enriched non-disease pathways.The x-axis represents the enriched non-disease pathways with which the Click here for additional data file.10.7717/peerj.5743/supp-15Supplemental Information 15ntigen processing and presentation pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the antigen processing and presentation pathway and each of the enriched non-disease pathways.The x-axis represents the enriched non-disease pathways with which the aClick here for additional data file.10.7717/peerj.5743/supp-16Supplemental Information 16apoptosis pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the apoptosis pathway and each of the enriched non-disease pathways.The x-axis represents the enriched non-disease pathways with which the Click here for additional data file.10.7717/peerj.5743/supp-17Supplemental Information 17platelet activation pathway share DEGs. The signaling pathways are represented by striped bars whereas the non-signaling pathways are represented by solid bars. The y-axis denotes the number of DEGs shared between the platelet activation pathway and each of the enriched non-disease pathways.The x-axis represents the enriched non-disease pathways with which the Click here for additional data file.10.7717/peerj.5743/supp-18Supplemental Information 18The core proteins are represented by thick borders and the shell proteins by thin borders. The RA drug targets are indicated by red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the non-DNA-binding proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-19Supplemental Information 19The core proteins are represented by thick borders and the shell proteins by thin borders. Rectangle nodes represent the DNA-binding proteins. The RA drug targets are indicated by red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the other proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-20Supplemental Information 20The core proteins are represented by thick borders and the shell proteins by thin borders. Rectangle nodes represent the DNA-binding proteins. The RA drug targets are indicated by red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the other proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-21Supplemental Information 21The core proteins are represented by thick borders and the shell proteins by thin borders. Rectangle nodes represent the DNA-binding proteins. The RA drug targets are indicated by red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the other proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-22Supplemental Information 22The core proteins are represented by thick borders and the shell proteins by thin borders. Rectangle nodes represent the DNA-binding proteins. The RA drug targets are indicated by red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the other proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-23Supplemental Information 23The core proteins are represented by thick borders and the shell proteins by thin borders. Rectangle nodes represent the DNA-binding proteins. The RA drug targets are indicated by red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the other proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-24Supplemental Information 24The core proteins are represented by thick borders and the shell proteins by thin borders. Rectangle nodes represent the DNA-binding proteins. The RA drug targets are indicated by the red borders. The degree of differential regulation of the nodes is denoted as follows: red to grey–downregulation and green–upregulation. The proteins lying isolated in the subnetwork are connected to the other proteins of the directed osteoclast differentiation network.Click here for additional data file.10.7717/peerj.5743/supp-25Supplemental Information 25This file contains 2 networks: The directed ODP network containing 424 nodes and 1589 interactions, and the core network (directed), containing 82 nodes and 152 interactions. The legend for nodes and edges are provided as a third network, legend.sif. The nodes contain information about differential regulation, DNA binding and RA drug target proteins. Edges show activation, inhibition, binding and post translational modifications.Click here for additional data file.10.7717/peerj.5743/supp-26Supplemental Information 26This file contains two networks, the directed ODP network with 424 nodes and 1589 interactions, and the core network containing 82 nodes and 152 interactions. The legend is provided along with the directed ODP network. The directed ODP network shows the shell and core proteins.Click here for additional data file.


The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments. Thousands of mutations are detected for a cancer with the advances of DNA sequencing technologies. The mutation frequencies of most genes are very low (<5%) in all patients of a cancer . TherefoIt has been reported that certain pathways are frequently altered across patients of a cancer by mutations in different genes of the pathways . Therefohttps://github.com/dxsbiocc/C-Sub.In this study, we developed an empirical algorithm, called PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing multiple mutation profiles of lung cancer, three reproducible common mutated sub-pathways were identified. PathMG was also used to identify LUAD-specific and lung squamous cell carcinoma (LSCC)-specific sub-pathways, respectively. Based on the subtype-specific sub-pathways, we further identified the sub-pathways related to the prognosis of lung cancer. Similarly, we also identified common and subtype-specific sub-pathways for colorectal cancer (CRC). PathMG is available on the web at www.cbioportal.org/) database. The mutation profiles of 230 LUAD samples  database  on Octobi, we calculated the number of samples mutated in this pathway for a dataset with N samples, denoted as iM. To test whether the number of mutated samples in the pathway i was significantly more than expected by chance, random experiments were performed. For a cancer sample, we calculated the number of mutated genes from its real mutation profile. Simultaneously, to produce a simulated mutation profile for the cancer sample, we randomly selected the same number of genes from the background genes as mutated genes. The total Refseq genes were defined as the background genes. After a random experiment, a random mutation dataset with N samples was produced. For a pathway i, we could calculate the number of randomly mutated samples after a random experiment, denoted as iR. The random experiment was repeated n  times, which may be adjusted by users. Then, the probability ip, that the number of randomly mutated samples (iR) of the pathway i is greater than the number of real mutated samples (iM), is calculated as follows:For a pathway in a given sample, we assume that the pathway is mutated in the sample if at least one gene within the pathway is mutated . Then, fi > MiR, then r = 1H; Otherwise, r = 0H. The formula was used to calculate the p values of all pathways, and the p values were adjusted using the Benjamini-Hochberg (BH) method to control the false discovery rate (FDR).In a random experiment, if After identifying the significantly mutated pathways for a cancer, we further extracted common sub-pathways in each of the significant pathways by the greedy search algorithm. Here, the sub-pathways, covering more than 90% (a default parameter) of samples of this cancer, were defined as common sub-pathways. The detailed algorithm to identify common sub-pathways in a given pathway is shown as follows  of each gene. Select the gene with the largest f value as a seed for an initial sub-pathway.Step 2: For a certain connected component, the genes within the connected component are ranked according to the mutation frequency (denoted as Pf) when the gene is added to the sub-pathway.Step 3: For each of the direct interaction neighbor genes of the seed, calculate the increased coverage  and the number of genes in the sub-pathway is at least five (default five), the sub-pathway will be output as a common sub-pathway. The two parameters, the frequency to define common sub-pathway and the minimum number of genes for a sub-pathway, could be adjusted by users.Step 6: For all the connected components obtained from a significant pathway, repeat steps 2–5 to identify all the common sub-pathways.A and subtype B) of a cancer. The significant pathways are defined as subtype-specific mutated pathways. The p values are adjusted using the BH method to control the FDR.First, the Fisher’s exact test is used to identify the pathways that have significantly different mutation frequencies between two subtypes  and the genes with v>0 are defined as subtype A specific genes (denoted as set aG) and the genes with v<0 are defined as subtype B specific genes (denoted as set bG). Then, the two classes of genes (aG and bG) are used to identify subtype A specific and subtype B specific sub-pathways, respectively. Here, we example the process to identify subtype A specific sub-pathways to explain the algorithm.Step 2: For a certain connected component, calculate the mutation frequencies of each gene in subtype v| value among aG as a seed for an initial sub-pathway.Step 3: Firstly, select the gene with the largest |aG, calculate the increasing of coverage difference (denoted as Pv) between subtype A and subtype B when the gene is added to the sub-pathway.Step 4: For each of the direct interaction neighbor genes of the seed in aG will be added to the sub-pathway one by one according to their Pv value from high to low. When direct neighbor genes added to the sub-pathway increase the coverage differences, the genes will be remained in the sub-pathway as new seeds. Similarly, PathMG allows for at most one gene that doesn’t increase the coverage difference. Therefore, for a gene that cannot increase coverage difference, its direct neighbor genes will be added one by one according to their Pv value from high to low. If at least one neighbor gene could increase the coverage difference of the sub-pathway, the gene will be remained in the sub-pathway and its neighbor genes increased coverage will be used as new seeds; otherwise, the gene will be excluded.Step 5: The direct neighbor genes of the seed in aG in the connected component. Calculate the p values of Fisher’s exact test (p < 0.05) for the sub-pathway, and output the subtype A specific sub-pathway if its mutation frequency in subtype A is higher than subtype B with a predefined parameter (default 0.25). Equally, we can identify subtype B specific sub-pathways for the connected component.Step 6: Based on the new seeds identified in step 5, repeat step 4–5 until the seeds don’t have direct neighbors among A specific and subtype B specific sub-pathways, respectively.Step 7: For all the connected components, obtained from a subtype-specific pathway, repeat steps 2–6 to identify all the subtype http://ctdbase.org/)  database, which collected a total of 719 cancer genes . We alsose.org/) . Among tMaterials and Methods.Firstly, random experiments were done to identify the significantly mutated pathways. Then, the sub-pathways commonly mutated in at least 90% of patients were identified in each of the significantly mutated pathways. Here, considering the existence of large measurement variation and low quality of tissue samples, we defined the sub-pathways covering more than a predefined parameter  of patients rather than 100% of patients in a cancer as commonly mutated sub-pathways. The details of the methods were described in p < 0.05). For example, the PI3K-Akt signaling sub-pathway included 141 genes and 43 of them were cancer genes, which was unlikely to happen by chance (p = 4.28E−29).Using the 408 mutation profiles of Lung-data1, we firstly identified 116 significantly mutated pathways for lung cancer (FDR < 0.05). Then, three sub-pathways, PI3K-Akt signaling sub-pathway, olfactory transduction sub-pathway, and regulation of actin cytoskeleton sub-pathway, were identified as the common sub-pathways of lung cancer (p < 0.05).Similarly, 125 significantly mutated pathways were identified from the 619 CRC samples from CRC-data1. Among the sub-pathways identified from these significant pathways, three sub-pathways, each of which covered at least 90% of 619 CRC samples, were identified as the common sub-pathways of CRC .Based on the mutation profiles of 230 LUAD and 178 LSCC samples from Lung-data1, 43 pathways with significantly different mutation frequencies between LUAD and LSCC were identified using Fisher’s exact test (FDR < 0.05). Here, we developed an algorithm to identify subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC (p < 0.05), including 6 LUAD-specific sub-pathways and 7 LSCC-specific sub-pathways. In the two independent Lung-data2 and Lung-data3 datasets, all the six LUAD-specific sub-pathways were validated as LUAD-specific sub-pathways. For the seven LSCC-specific sub-pathways, six were validated as LSCC-specific sub-pathways. Only one sub-pathway, inositol phosphate metabolism, had p value less than 0.05 in both two validation datasets, but its mutation frequency difference was 0.18 and 0.12 in Lung-data2 and Lung-data3, respectively  . All the 42 sub-pathways were RCC-specific, which further validated the report that RCC was hyper-mutational .Similarly, 221 subtype-specific pathways were identified for CRC using the mutation profiles of 166 LCC and 315 RCC samples from CRC-data1 . Based on the subtype-specific pathways, 42 subtype-specific sub-pathways were further identified with mutation frequency difference higher than 0.25 , five of the six common sub-pathways covered at least 86% samples, whereas the remained sub-pathway of phospholipase D signaling pathway covered 84% samples in both the two validation datasets  for the publication of any potentially identifiable images or data included in this article.ZG, HY, and XD conceived and designed the overall study. HY, XD, HC, JC, JH, QG, ML, JXie and JXia analyzed the data. HY, YG and ZG wrote the manuscript. HY, XD and HC revised the manuscript. All authors read and approved the final manuscript.This work was supported by the National Natural Science Foundation of China [grant numbers. 61801118]. The education research project for young and middle-aged teachers in Fujian province [grant number. JAT170214] and the Fujian natural science foundation [grant number. 2019J01678].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.


Interpreting changes in metabolite abundance in response to experimental treatments or disease states remains a major challenge in metabolomics. Pathway Covering is a new algorithm that takes a list of metabolites (compounds) and determines a minimum-cost set of metabolic pathways in an organism that includes (covers) all the metabolites in the list. We used five functions for assigning costs to pathways, including assigning a constant for all pathways, which yields a solution with the smallest pathway count; two methods that penalize large pathways; one that prefers pathways based on the pathway’s assigned function, and one that loosely corresponds to metabolic flux. The pathway covering set computed by the algorithm can be displayed as a multi-pathway diagram (“pathway collage”) that highlights the covered metabolites. We investigated the pathway covering algorithm by using several datasets from the Metabolomics Workbench. The algorithm is best applied to a list of metabolites with significant statistics and fold-changes with a specified direction of change for each metabolite. The pathway covering algorithm is now available within the Pathway Tools software and BioCyc website. As interest in metabolomics grows, so does the number of data sets. For example, during the past three years (December 2015–2018), the available studies deposited in Metabolomics WorkBench [Pathway covering  is a newThe classic set-cover problem starts with a “universal” set of elements and a collection of sets of elements drawn from that universal set. The goal is identifying a smallest collection of these sets that covers the entire universal set of elements. One extension of this problem is to apply weights or costs to each of the sets that are combined to form a cover. This is called the weighted set-cover problem. We will refer to these weights as costs throughout this paper as our optimizer is designed to seek a minimum value solution.In this work, the set to be covered consists of metabolites observed to have changed due to disease or experimental conditions, and the collection of sets meant to cover the input set of metabolites consists of the collection of sets of substrate compounds for each pathway in the organism. In this application, we assign costs to the pathways themselves, rather than their sets of substrate compounds.together includes as many of the given input elements (metabolites) as possible andrepresents a “minimum cost” way of doing so.The problem is finding a collection of sets (pathway substrates) that:Assigning a cost to each pathway and then solving for a minimum-cost solution reduces the possible solutions to sets of pathways that are small, thereby avoiding “solutions” such as the set of all pathways that contain one or more substrates on the list. Different analysis goals will suggest different cost functions that will yield alternative solutions. Such goals might include the absolute smallest number of pathways, or pathways that are small or have a large a proportion of input substrates, or pathways with a particular function or class such as synthesis or degradation.The problem of finding the smallest covering set is one of the well-known NP-hard problems , and sevIn practice, the only input metabolites that can be included in a covering solution are those that are recognized and are substrates of at least one pathway in the database. Many compounds in organism metabolic databases, such as HumanCyc, are not assigned to any metabolic pathway. In addition, pathways include compounds not found in the input set. These compounds can be removed from consideration as they have no bearing on whether a pathway will cover compounds in the input set. By extension, any pathway with no compounds in the input set is eliminated from further consideration.Therefore, the pathway covering problem is to cover the subset of input metabolites that are known to be substrates of at least one pathway, using the subsets of the substrate compounds of pathways that occur in the input set. The optimal solution, at this point, is the smallest set of pathways that cover the compounds in the input set.This approach will always yield the smallest collection of pathways or, in some cases several collections of pathways of equal size. However, this solution may not be the most biologically meaningful. For example, using the constant cost function will prefer larger pathways that include more input compounds, since fewer pathways will be needed for the cover. These larger pathways are more likely to have branches and alternative routes such that a particular compound may not be essential to the pathway, whereas a smaller pathway is less likely to have alternative routes. There may also be prior justification to focus on pathways of a certain type .We explored more than a dozen cost functions, but we will report results for the five functions (enumerated below) that we are making available in the released version of the tool.“Metabolic Profiling of Visceral and Subcutaneous Adipose Tissue from Colorectal Cancer Patients: GC-TOF MS analysis of subcutaneous and visceral adipose tissue samples” (Metabolomics Workbench (MWB) repository study ST000061; ). This s“Metabolite-Phenotype Link in X-Linked Adrenoleukodystrophy (Fibroblast Cell Culture)” . This s“Integrated phosphoproteomic and metabolomic profiling reveals NPM-ALK-mediated phosphorylation of PKM2 and metabolic reprogramming in anaplastic large call lymphoma”  . Agreement within input or output covered metabolites are scored more highly than agreement within intermediates, and complete agreement is scored more highly than majority agreement. This function does not compare change directions between groups.Our primary results consist of a pathway covering analysis of the three selected studies using five different cost functions. A more detailed description of the cost functions appears in the methods, but in summary, the cost functions are:From each study we extracted lists of significantly changed compounds. In two of the studies  we extracted separate lists of compounds that increased and decreased concentrations between the conditions compared in the study. The third study (ST000061) compared two cell lines and all significant concentration changes were in one line. Several compounds required manual resolution as the name provided by Metabolomics Workbench could not be automatically resolved. In the course of manual resolution, we identified several compounds that were found in the more extensive MetaCyc database from which HumanCyc was partially derived.d-glucopyranose and beta-d-glucopyranose) have reactions that occur in pathways and need to be considered. Rather than trying to resolve the ambiguity, we included all possible interpretations.In some cases, an input compound name was ambiguous; it either referred to more than one compound or more commonly, it referred to what HumanCyc considered to be a class of compounds. For example, “Glucose” refers to a compound class that ultimately has three compound instances (leaves of the ontology tree), of which two  from 50 colo-rectal cancer patients, though 59 samples are reported for both cell types. All significant changes were in the direction of higher metabolite levels in the visceral tissue. There were 39 named compounds  that shoThe constant cost function generated over 1 million solution sets, and apart from a representative solution of 20 pathways listed in alpha-tocopherol covered by alpha-tocopherol degradationarachidonic acid and ethanolamine covered by anandamide degradationcholesterol covered by pregnenolone biosynthesisL-isoleucine covered by isoleucine degradationL-leucine covered by leucine degradationL-tyrosine and L-phenylalanine covered by tyrosine biosynthesisL-threonine covered by threonine degradationL-valine covered by valine degradationputrescine and L-ornithine covered by putrescine biosynthesis IIIOf the 35 metabolites resolved from the 39 names reported in the study, 12 cases had a metabolite that was consistently covered by a particular pathway, across all solutions returned by any cost function.For eight compounds , the pathway selected was the only one in the database that specified the particular compound (as opposed to a class of compounds) as a substrate. Likewise, the presence of phenylalanine pulled in its degradation pathway which removed any advantage to including tyrosine in any other pathway. The absence of synthesis pathways is consistent with six of the seven listed amino acids being considered essential in humans. Likewise, alpha-tocopherol is a nutrient that humans cannot synthesize (vitamin E). Although both arachidonic acid and ethanolamine are specific substrates of multiple reactions in pathways, anandamide degradation is the only pathway in which they both occur.The small solution set size returned by the compound sparseness cost function may reflect the function consistently covering seven compounds with one superpathway (“superpathway of conversion of glucose to acetyl CoA and entry into the TCA cycle”). The constant cost function also did this for at least some solutions. The remaining cost functions, size, and biosynthesis preferred, covered four of these seven with a smaller pathway (“Rapoport-Luebering glycolytic shunt”).However, these four compounds were all introduced to the covering procedure by one input name (“glyceric acid”) which in HumanCyc is a class with four instance compounds. The glycolytic shunt is active in mammalian erythrocytes but is not reported to be active in adipose tissue.S of the covering set returned for the study data (20) against the distribution, S was smaller than 994 coverings and equal in size to three coverings of random compound sets. This suggests that the pathway covering tool is finding a signal in the observed metabolite shifts that is absent in random collections of metabolites in pathways. As such it validates both the method and the dataset.Intuitively, if the input metabolite set represents a coordinated shift in metabolism as a result of the experiment, the size of its pathway covering set should be smaller than the pathway covering set for a set of metabolites of the same size, drawn at random from pathways in the organism. This notion was tested by generating 1000 sets of 34 compounds, drawn without replacement from all compounds that were substrates of at least one pathway. The covering algorithm was applied to each set, using the constant value cost function, and the size (number of pathways) in each solution was collected into a size distribution. Comparing the size To compare pathway covering with enrichment, we performed a “compounds enriched for pathways” analysis of the 39 compounds used in the pathway covering analysis using HumanCyc as the database (see methods for details). A pathway enrichment analysis of the ST000061 data set (see Methods for details) returned ten individual pathways (and 13 pathway classes). We compared this result against the solutions returned by pathway covering using the covered compound sparseness function. The covering analysis returned four solutions, sharing 19 pathways with an additional five pathways appearing among alternative solutions Table A4n = 6 per condition) of patients with mild or severe ALD disease or healthy controls. The input list consists of extracted metabolites showing differences between the healthy and severe disease conditions. The compounds are listed in This study compared metabolite levels in fibroblast cell cultures collected from a small number (docosahexaenoic acid covered by aspirin triggered resolvin D biosynthesisn-acetyl-beta-neuraminate covered by CMP-N-acetylneuraminate biosynthesis I (eukaryotes)N omega-phosphocreatine covered by creatine-phosphate biosynthesisnicotinamide covered by NAD salvageophthalmate covered by ophthalmate biosynthesissn-glycero-3-phosphocholine covered by plasmalogen degradationd-fructofuranose and keto-d-fructofuranose covered by sorbitol degradation Ibeta-d-glucopyranose and alpha-d-glucopyranose covered by trehalose degradationbeta-alanine and taurine covered by tRNA-uridine 2-thiolationOf the 20 input metabolites, 12 cases had a metabolite that was consistently covered by a particular pathway, regardless of the cost function.N-acetyl-beta-neuraminate, omega-phosphocreatine, nicotinamide, ophthalmate, sn-glycero-3-phosphocholine, choline, beta-d-fructofuranose, and keto-d-fructose), the pathway selected was the only one in the database that specified the particular compound. Likewise, the pairs beta-d-glucopyranose–alpha-d-glucopyranose and alanine–taurine were only found together in those particular pathways, though the first pair is the result of a disambiguation. This led to relatively few differences between cost functions. The constant and pathway harmony functions did pick the larger “superpathway of conversion of glucose to acetyl CoA and entry into the TCA cycle” over covering the same three compounds with the TCA cycle pathway. Since exactly the same three compounds were covered, this case illustrates the shortcoming of cost functions that ignored pathway size versus those that explicitly  or implicitly (covered compound sparseness) incorporated size.For eight compounds . The later included six additional pathways. Unlike ST000061, this result shows that enrichment and pathway covering can substantially overlap.A pathway enrichment analysis of the ST000741 compounds yielded the unexpected result that no pathways were returned when the Benjamini-Hochberg correction was applied, even with a cutoff analysis  uses thep = 0.05 level. McDonnell et al. used a combination of phosphoproteomics and metabolomics to highlight changes in metabolism brought about by NPM-ALK. Their conclusions were derived from a range of methods. Here, we focus on the list of Metabolites in their Supplemental Table S2, which they claim are metabolites regulated by ALK. The table compares normalized metabolite abundance in control cells that were treated with DMSO and cells from the same line that were treated with an ALK inhibitor (CEP). No significance levels were reported for these metabolites, but fold-changes were, so, for consistency, we applied the same 1.5 fold-change cutoff as was done with the MWB studies. Figure 3 in their publication indicates that the list of metabolites may have been filtered at the sn-glycero-3-phosphocholine covered by plasmalogen degradationlactate covered by pyruvate fermentation to (S)-lactate.palmitate covered by stearate biosynthesisAMP covered by stearate biosynthesisOf the 19 metabolites resolved from the 24 names reported in the study, four metabolites were consistently covered to a particular pathway, regardless of the cost function.Only one compound, sn-glycero-3-phosphocholine appeared in the database as a substrate only of its covering pathway. The inclusion of AMP, although appearing in many pathways, is the likely reason that palmitate  was never covered by its own synthesis pathway.We compared the size of the covering set calculated with the constant cost function against the distribution of covering sets from 1000 sets of 19 metabolites generated at random from the metabolites with pathways in the HumanCyc database. The observed size of eight was smaller than any covering from a randomly generated set. This is not surprising since the list of compounds in the supplemental list was certainly not random.McDonnell et al.  interprePathway enrichment of the McDonnell et al. data set returned 44 individual pathways that included nine of the ten pathways that the single solution from covered compound sparseness . The enrAny method that interprets metabolite measurements in terms of changes in pathways will necessarily be dependent on the database of pathways used for interpretation. Furthermore, the requirements that a compound is in the database, is recognized by name, and exists in at least one reaction and one pathway is common to all pathway-based interpretations of metabolomics data. The necessity to identify reactions and pathways led us to avoid studies with a large lipidomics component. Lipids pose a particular challenge since there are relatively few pathways known that are specific to particular lipid species even though they are easy to detect and identify with current methods. The solution lies in continued growth and curation of pathway databases, along with the caution that results with this method will differ between databases with different sets of metabolites and pathways.There are several other approaches to the interpretation of metabolite lists in terms of affected pathways. A review by Booth et al.  divided The review by Marco-Ramell et al. covered 13 different tools. These included cross database tools such as MetaboAnalyst and its tools MSEA  and MetPp-value, either because of the pathway’s size or other properties of the database, nothing is reported. By contrast, the pathway covering algorithm assigns every compound to a pathway if such a pathway exists in the database, regardless of statistical significance. There will also be differences depending on the choice of statistical method (Hypergeometric/Fisher’s exact vs. Kolmogorov-Smirnov) and the correction for multiple comparisons .p-values do not reach statistical significance , which can be an advantage if in fact a single metabolite is a valid signal for a pathway whose flux is indeed changing. In particular, enrichment methods may tend to miss large pathways since to exceed the p-value cutoff many metabolites for such pathways must be in the observed set. We expect pathway enrichment to return more results than pathway covering when a larger number of metabolites are observed, resulting in more pathways that exceed the p-value cutoff. One of our motivations for developing the pathway covering method was that enrichment sometimes returns many “overlapping” pathways that are all triggered by a small number of metabolites that are found in multiple pathways.When we compared the results of enrichment and pathway covering empirically, we found that in one case (ST000061) pathway covering returned more pathways, and in two cases enrichment returned substantially more pathways. Our interpretation is that pathway covering will return more results when enrichment Visualization approaches that center on interpreting metabolomics data focus on visualizing metabolites (either presence on a list or level of change between samples) on a diagram, either as a set of pathways or metabolic overview diagram. Examples include multiple tools in the Pathways Tools suite , also thPathway covering, as implemented on the BioCyc website, provides visualization of metabolites in pathways but is really an alternative to enrichment methods. It differs from enrichment methods in that it can propose alternative solutions, both because of alternative cost functions and because of the existence of multiple solutions with the same objective function value. Enrichment approaches, for example, simply count compounds and pathways, whereas cost functions can focus on properties of pathways in addition to whether the pathway contain a particular compound as a substrate (as exemplified by our pathway harmony function).Pathway covering proposes a small set of pathways, which potentially makes a solution easier for the user to evaluate than either a large set of enriched pathways, or a graphical display of compounds mapped onto a large and complex metabolic map diagram.We evaluated five cost functions. Although in many cases an input compound will be mapped to a particular reaction regardless of the cost function, the functions did produce different results. The most important difference was the number of alternative solutions generated by a particular cost function. Although it is desirable to produce small, dense covering sets that reflect a strong signal in the data, a cost function that results in a multitude of solutions will also hide any such signal.Although the constant cost function is the simplest method and will produce the smallest solution sets, it suffers from being the most prone to multiple solutions and will, more than any other cost function, choose large superpathways over smaller sets of pathways that cover the same compounds. The pathway size and covered compound sparseness functions avoid these issues, but by using the proportion of covered input compounds to other substrates, covered compound sparseness makes better use of the provided information and seems to be intermediate between pathway size and constant cost in its selections. It was also the best at returning single or very small sets of solutions. The biosynthesis-preferred function did show the preference intended, but otherwise behaves like pathway size. Finally, pathway harmony is the only cost function that does distinguish between increasing and decreasing compounds in the input list, and when it detects a pattern in a pathway’s reactants or products, it does produce unique pathway choices. However, when the majority of pathways cover only a small number of compounds, pathway harmony acts very much like the constant weight function, and leads to the same problem of large numbers of alternative solutions. The use of pathway harmony is strongly discouraged for data sets where increases and decreases in compound levels are not specified, since behavior resembling the constant cost function is almost guaranteed.Unless there is a specific need for a focus on a focus on synthesis, we recommend the Covered Compound Sparseness or Pathway Harmony (when appropriate) cost functions.brg-preview.ai.sri.com.) The user first selects the organism database that will supply the pathways for which the covering will be computed. The tool next reads a file of metabolites in a one-compound-per-line format, with an optional second column indicating whether the compound increased (“+”) or decreased (“−”). The tool enables the user to select among the cost functions described in here.The Pathway Covering tool is available as part of BioCyc.org and the Pathway Tools software, version 23.0 and higher, under the web Analysis menu. . On a separate browser tab, the set of compounds in the covering solution will be shown, giving the pathway’s score and a small diagram of the pathway with each covered compound’s location in the pathway highlighted.The Common Lisp source code, which includes additional APIs and cost functions, is available in the “Metabolic Profiling of Visceral and Subcutaneous Adipose Tissue from Colorectal Cancer Patients: GC-TOF MS Analysis of Subcutaneous and Visceral Adipose Tissue Samples,” which was Metabolomics Workbench Study ST000061.“Metabolite-Phenotype Link in X-linked Adrenoleukodystrophy (Fibroblast Cell Culture),” which was Metabolomics Workbench Study ST000741.“McDonnell et al.” used supplementary data from We selected three human data sets, two from MWB and the other supplementary data to a publication.p-values of 0.05 and 1.5-fold abundance differences in the screening.Although the software will work with any of the more than 14,000 PGDBs available in BioCyc, we focused on human metabolomics data because of the large number of human studies and availability of HumanCyc, a highly curated PGDB. The selection process focused on studies that had sizable numbers of non-lipid metabolites that showed significant and sizable differences between groups. The volcano plot and ANOVA tools in Metabolomics Workbench were used for the initial screening; we used (Benjamini-Hochberg corrected) For the McDonnell et al. study, we used the list of metabolites and the same 1.5-fold abundance difference for screening.Study ST000741 and the McDonnell list yielded lists of metabolites that increased or decreased. The implementation supports entering the list of increased and decreased metabolites separately, but the lists are combined internally except when the pathway harmony cost function is used. All the differences in study ST000061 were in one direction.Constant—This function returns a constant cost for every pathway. This cost function corresponds to treating pathway covering as a special case of set covering; all pathways are treated equally.Pathway Size—This function returns the number of reactions in the pathway as its cost, so that small pathways are preferred to large pathways.Biosynthesis Preference—This function starts with the pathway size cost, then, unless the pathway is a biosynthetic pathway, as determined by its location in the MetaCyc pathway ontology, the cost is multiplied by two and one is added.Covered Compound Sparseness—This function prefers pathways that contain a large fraction of the input metabolites. The function computes a set S, the substrate compounds for all reactions in the pathway. It divides S into two subsets:M), which we call R—the remaining substrates.The multiplication and rounding assures an integer coefficient for the solver.Pathway Harmony—This function favors pathways where either the “input” substrates are metabolites that show an increase in abundance, and the “output” substrates are metabolites that show a decrease, or vice versa. This is the only cost function that distinguishes metabolites that increase or decrease in abundance. It divides the substrate compounds in the pathway into three groups:-R),compounds that are reactants, but not products of any reaction in the pathway (-P), andcompounds that are products, but not reactants of any reaction in the pathway (-I)intermediates  when all compounds agree in direction within a group. Cases where most compounds in a group are in the same direction  are given a smaller cost advantage. For more details, refer to the calculate-harmony function in the source-code Pathway harmony focuses on agreement within each of the three classes of substrates as indication that the experimental change or disease condition is affecting the pathway in a consistent way. A high flux through the pathway should increase the levels of all three classes, but if the pathway represents a bottleneck, the direction of change between the substrates might differ.We consider five cost functions in this study. They are summarized here:(1)Select the BioCyc PGDB for the organism in which the metabolomics study was performed.(2)Identify pathways in that PGDB that contain at least one substrate compound from the input set. These are subsequently referred to as candidate pathways. Other pathways (those with no substrate compounds in the input set) are ignored, since they would not be in the solution.(3)Calculate the cost of each candidate pathway using the user-selected cost function.(4)Use the costs and the compounds associated with each pathway to construct an integer programming problem.(5)Solve the integer programming problem for the optimum value of the objective function(6)If multiple solutions are desired, add the optimum value as a constraint to the integer programming problem and run the solver in multiple solution mode.Computing a pathway covering is a process of five or six steps, depending on whether multiple solutions are desired.We use the SCIP solver ,27, versi is in a solution and i.The integer programming problem is specified as an objective function to minimize, and a set of constraining inequalities. The objective function is created from all input pathways, summing the cost of each pathway in the solution set:The reminder of the problem consists of constraints, expressed as inequalities, one per compound in the input set, to ensure that each compound is covered by at least one pathway in the solution given:A is covered only by pathways 1, and 2 and compound B is covered only by pathways 2 and 3 then the constraints are:For example, if compound All such constraints can be satisfied, because the preprocessing of the input metabolites and pathways has ensured that each input metabolite is covered by at least one pathway.These constraint inequalities and the objective function are passed to the solver for the case of solving for a single solution. The solver will return both a solution and the corresponding (optimum) value for the objective function. If multiple solutions are requested, an additional constraint is added where the value of the objective function is less than one more than the previously returned optimum value .A worked-through example appears in The covering algorithm ignores non-metabolic pathways such as transport and specifically signaling pathways, and the “pathway” comprised of the amino acid charging reactions for each tRNA.We generated random sets of metabolites by gathering all the substrates of reactions within pathways (some reactions are not part of pathways in HumanCyc) into a single list. For each study, a list of 1000 sets of random metabolites of size equal to the number of metabolites for each study was generated. The metabolites on these lists were limited to those found in pathways. As the purpose of this analysis was determining that the pathway covering was detecting some signal in the metabolite input, the simplest and most compact cost function (constant) was used for the comparison of each study and generation of the distribution of pathway sizes.p-value cutoff of 0.1 as representing typical settings one would use in an exploratory analysis. Please note that for study ST000741, as noted in the results, applying any correction resulted in no enriched pathways with a p-value less than 0.3, so the set of pathways reported are those with no correction applied.We performed pathway enrichment analysis on each of the three datasets. We used BioCyc’s SmartTables enrichment analysis to determine pathways enriched for each set, using HumanCyc as the selected database. We specified a Benjamini-Hochberg correction for multiple comparisons and a