Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes. Such alterations include mutations, copy number alterations, DNA structure variants, epigenetic alterations, and other genomic variations2. Driver SGAs in a tumour activate the oncogenic process by perturbing genes in cellular signalling pathways that regulate homeostasis2. Cancers are heterogeneous in that tumours originating from the same tissue often exhibit significantly different molecular and clinical phenotypes, leading to differences in responses to treatments and patient survival. This well-known inter-tumour heterogeneity is largely due to distinct disease mechanisms underlying the development of an individual tumour, potentially resulting from different compositions of pathway aberrations. Understanding disease mechanisms of an individual tumour and further identifying common patterns of disease mechanisms among a cohort will not only provide insights into cancer biology but can also guide personalized therapy.Cancer is a complex genetic disease, mainly caused by somatic genome alterations (SGAs) that affect oncogenic processes3, among which the majority has relatively low-occurrence frequency in a tumour cohort. As a result. it is difficult to find sufficient patterns in SGA events. Second, among all the SGAs observed in a tumour, usually a small fraction directly promotes tumour development (drivers), whereas the majority of other SGAs is non-consequential with respect to oncogenesis (passengers)10. Identifying driver SGAs underlying the development of an individual tumour remains a major challenge in cancer genomics, which in turn makes it difficult to find co-occurrence patterns of cancer-driving SGAs. Third, an oncogenic pathway can be perturbed by different SGAs affecting distinct members of the pathway9. For example, the phosphoinositide 3-kinase (PI3K) pathway can be aberrantly activated by mutation/amplification of PIK3CA, mutation/deletion of PTEN, or mutation of AKT112, and so on. There is no simple way to determine whether two distinct SGA events observed in two different tumours are affecting a common pathway. The above challenges make it difficult to use SGA data to determine which pathways are aberrant in a tumour and to further identify combination patterns of pathway aberrations.So far, it remains a challenge to discover disease mechanisms of cancers solely based on SGA data of tumours for the following reasons: First, a tumour usually hosts from hundreds to over a thousand SGA events15. However, while current approaches can identify genes differentially expressed in different subtypes of cancers, it is unclear which pathways drive their differential expression. Furthermore, current efforts in using gene expression patterns to find cancer subtypes can be heavily influenced by cell-type-specific expressions, leading to subtypes that are divided based on the origins of cells rather than disease mechanisms. For example, some breast cancer subtypes are based on the cell of origin, such as basal vs. luminal cells. In general, it would be ideal to identify a module of genes whose expressions are regulated by a specific oncogenic pathway, so that one can use expression status of such modules to discover combination patterns of pathway aberrations and classify tumours according to disease mechanisms rather than the tissue of origin.On the other hand, gene expression profiles have been widely applied to identify molecular subtypes of cancers through clustering analysis, which leads to the discovery of subtypes among cancers originating from a common organ or tissue, and, in many cases, transcriptome-based subtyping reveals different outcomes and thus different responses to therapies16. TCI infers the causal relationship between SGAs and somatic genome alterations (DEGs) within an individual tumour 17. TCI analysis was applied to each of these tumours, which identified tumour-specific causal relationships between SGAs and DEGs in a tumour. Through a series of analyses of the pooled results from all tumours we further identified a set of candidate driver SGAs and their signature DEGs16. We then set out to construct a network of the signature DEGs of a specific cancer type to represent the co-regulation relationships among the DEGs. Specifically, each node in the network represents a DEG, and an edge was added between two DEGs if they were co-assigned to the same SGA by TCI in at least one-tenth of the tumours of this cancer type. The edge weight is proportional to the number of tumours in which the pair were co-regulated by a common driver SGA . Our assumption is that the higher frequency that two DEGs are co-regulated by a common set of SGAs, the higher probability that these two DEGs are regulated by the same upstream signalling pathway perturbed by these SGAs. The DEG networks of BRCA and GBM were constructed using TCI results from 874 BRCA and 143 GBM tumours, respectively. The resulting networks contained 1,747 DEG nodes for BRCA and 3,576 DEG nodes for GBM.We collected omics data of 5,097 tumours from the Cancer Genome Atlas (TCGA) dataset18. Specifically, we repeatedly performed spectral clustering with different random initializations of cluster centres (see Methods) and then conducted a consensus clustering analysis by pooling the results and identifying DEGs that were consistently assigned to a common module during the experiment. Using this approach, we identified 7 DEG modules for BRCA and 15 for GBM, each containing from a few DEGs to over hundreds of DEGs Gene expression data have long been used to cluster tumour samples into subgroups, in which expression signatures associated with each subgroup are identified. However, it remains a challenge to determine which aberrant pathways drive the changed expression of signatures associated with such tumour subgroups and further utilize such information to understand distinct disease mechanisms. A key advantage of our framework is that we can estimate the SGAs underlying the co-regulation of members of a DEG module, i.e., the drivers of a DEG module that potentially perturb a common pathway. Each DEG module identified with spectral clustering contained a group of genes that were frequently co-regulated by the same set of SGAs. Accordingly, we extracted the SGAs that underlie the co-regulation for each DEG module. We called an SGA a dominant SGA of a DEG module if it produced over 10% of the co-regulation instances between DEG pairs in the module. Although for each DEG module there could have been hundreds of SGAs that contributed to its co-regulation instances, usually about three SGAs turned out to be dominant. The dominant SGAs together are responsible for about 90% or more of all the co-regulation instances. Different DEG modules had distinct dominant SGAs, although certain members overlapped Tables . This inZFHX4 and RYR2, are well-known drivers of BRCA22. ZFHX4 has been found to play a role in maintaining tumour cell state in GBM23, and our previous experimental study indicate that it does regulate the expression of certain target genes predicted by the TCI algorithm16. On the other hand, while some studies suggest that alterations on RYR2 are likely passenger events, TCI consistently discovered that SGAs in RYR2 have impact on certain DEGs. Therefore, we propose ZFHX2 and RYR2 to be novel drivers for BRCA. For GBM, most dominant SGAs are known drivers of this cancer type25 except MARCH9, AGAP2-AS1 (AGAP2 antisense RNA 1), CHIC2, GSX2, RYR2, MTAP, and TTN. For these genes, except MARCH9 and TTN, there is literature supporting that they are potential novel drivers of GBM. Specifically, AGAP2-AS1 and GSX2 are known to be associated with neuron system development27 and, therefore, alterations on these genes could be exclusive drivers of GBM. CHIC2 has been found to be associated with myeloid leukemia28, and MTAP has been proposed as a tumour suppressor for BRCA29. For MARCH9, on the other hand, we consider it to be a passenger because it is on the same chromosome location 12q14.1 as AGAP2-AS1; they are frequently co-affected by the same genomic alteration event. TTN was found to be associated with BRCA and other cancer types31, but it is generally considered to be a passenger as its long polypeptide structure may bias its mutation frequency15.For BRCA, all dominant SGAs, except CDH1 and GATA3 are the first two dominant SGAs of BRCA’s DEG module 1, and they are also two well-known drivers of BRCA22. 50.1% of TCGA BRCA samples (891 samples from the input data of TCI) have mutations in CDH1, GATA3, or PIK3CA, which suggests module 1 as the most associated function module with the disease mechanism of BRCA. With dominant SGAs PTEN and PIK3CA, DEG modules 2 and 7 represent the PI3K/Akt signalling pathway, which is known as one of the most commonly activated pathways in cancer32. The sharing of the dominant SGA PIK3CA across modules 1, 2, 5, and 7 suggests that although each module is considered to perform a relatively independent function, they are communicating with each other through interactions within a common signalling pathway. Module 3 contains two novel drivers, ZFHX4 and RYR2, which cover 44.7% and 22.9% edges (pairs of DEGs) respectively. This may represent a novel functional module that would support the development of BRCA for some subgroups of patients (dominant SGAs mutations found in 18.2% samples). Module 4 has only one dominant SGA, GATA3, which represents the module resulting from a single driver rather than from the interactions between multiple drivers like module 1. Module 5, with its most dominant SGA being ERBB2, represents another important signalling pathway in BRCA, the ErbB/HER signalling pathway33. Module 6, on the other hand, represents the most commonly inactivated pathway in cancer, the p53 pathway34. Therefore, some of the DEG modules we identified for BRCA are more representative for general cancer signalling pathways, whereas others are more specific to a particular cancer type.Based on the dominant SGAs, we can infer what signalling pathway or function module each DEG module represents. PTEN, communicate with each other through the PI3K/Akt signalling pathway. Modules 3, 8, 10, and 12, with the most dominant SGA being CDKN2s (commonly deleted in GBM)24, represent function modules controlled by the cell cycle process. Modules 6, 7 and 11, with dominant SGAs being SEC61G and EGFR that were found specifically amplified in GBM35, represent the EGF/EGFR pathway. Modules 4, 9, 13, and 14, which have the most novel drivers, are potentially newly discovered functional modules that guide tumour development for some subgroups of GBM patients .Similarly, in GBM, module 1 represents the p53 pathway. Modules 2, 5, and 12, sharing the dominant SGA 20, which has relatively complete gene expression and survival data of close to 2,000 breast cancer patients. For GBM, we used the gene expression and clinical data provided by the TCGA. The BRCA feature dataset used for clustering patients consists of the constructed DEG module features and 8 clinical features we had collected from the METABRIC dataset. The GBM feature dataset consists of the constructed DEG module features and age at diagnosis . Patient subgroups were identified using Partitioning Around Medoids  consensus clustering, as consensus clustering generally produces more robust and consistent clusters36. PAM was selected, for it provides a centre of each resulting group with which new data can be classified, an advantage compared to the hierarchical clustering and it is generally more robust to noise and outliers than k-means37. When all clinical features and DEG modules were used, 5 and 6 patient groups were identified for BRCA and GBM, respectively . To this end, we used the dataset from a study by the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)ely Figs  and S6. ely Figs  and 4b some Fig. . For GBMPTEN and PIK3CA); its grade of disease is also higher, which resulted in its relatively lower survival chance. Group 5, having the worst survival outcome, contains most patients as Her2+. In summary, the survival of BRCA subgroups is strongly related to their clinical features such as age and protein-based biomarkers . Given the similar clinical features, the pattern in DEG modules determines the survival difference. For GBM  compared to group 2. Groups 3 and 4 distinguish themselves with their different distributions of DEG module values, especially in their reversed pattern in DEG modules 1–5. Although they both contain younger patients, their different values in DEG modules suggests that they have different combinations of signalling pathways being defective, which resulted in a much higher survival fraction of group 4 than group 3  patients and IDC  + ILC patients were clustered in patient groups 1, 2 and 4 . This agrees with previous studies that ILC patients are mostly ER + tumours classified as luminal A subtype22. Group 3, the triple-negative group, is dominated by basal-like patients , which indicates that the GBM patient groups are more survival indicative compared to the TCGA subtypes.We compared our GBM patient groups with the four GBM subtypes established by TCGA, 201014 Fig. . Group 114 Fig. . Recall 14 Fig. , where tnts Fig. . These tnts Fig. , which snts Fig. . The neu−12 and 9.75 × 10−3 for BRCA and GBM, respectively, Figs To examine the power of genetic features alone in predicting patient survival outcome, a second PAM consensus clustering of patients was completed using only the DEG modules as features. This also gave rise to a division of BRCA data into 5 patient groups, and a division of GBM data into 6 patient groups Figs  and S8. 46. For the patient-group-specific models, each patient group has a different combination of clinical features as significant . The DEG modules that are generally significant across all-patient and DEG patient groups are modules 1, 2 and 5. Modules 1 and 2 are positively correlated with the hazard rate, and module 5 is negatively correlated with the hazard rate. These partially explain the survival curves we observed above. With high value in module 2, patient group 4 has a much lower survival fraction compared to patient group 2, even though their other DEG modules are comparable. The lower average value in module 2 also resulted in a better survival outcome of DEG patient groups 2, 5 and 1. Note that the dominant SGAs of module 2 are PTEN and PIK3CA; a high value in this module represents activation of the PI3K/Akt signalling pathway that is known to be related to ILC22.In order to evaluate the contribution of each feature towards survival estimation, we trained a Cox regression model using all features as covariates for all patients as a whole and for each patient group separately Table . To compEGFR. EGFR has been used as the primary marker in distinguishing between GBM patients and it was found to interact with multiple signalling pathways in GBM47. In addition to module 11, the set of significant DEG modules are mostly mutually exclusive across patient groups. In other words, even though GBM patients generally share similarly undesirable survival outcomes, their survival rates can be explained by different combinations of genetic features. This suggests that each of them took a different disease mechanism in their tumour developments. For example, module 7, the smallest DEG module with dominant SGA EGFR and TP53, has a high diversity across patients. This module represents the result of the communications between the Glioma pathways (KEGG map05214), which are known to explain the disease mechanism for both primary and secondary GBM48. In addition, the C-index of GBM Cox regression models is higher in the patient-group-specific model than in the overall model, which also supports the idea that different patient groups underwent different disease development procedures that should not be mixed. Three patient groups, 4, 5 and 6 (together containing 225 patients), have a C-index over 0.7, which is higher than a previously reported Cox regression model trained on a subset of TCGA GBM patients using clinical and imaging features (0.69)49.Unlike BRCA, where clinical features dominate survival estimation, most GBM Cox regression models contain several DEG modules as significant covariates. The most common significant DEG module across patient groups and DEG patient groups is module 11, with its dominant SGA In this study, we designed  a novel computational framework, which utilizes the causal inferences between SGAs and DEGs for constructing expression and signalling state representations, in the form of modules of DEGs that reflect the major transcriptomic programs that are perturbed in a cancer type. We conjecture that different combinations of expression status of DEG modules potentially reflect different combinations of aberrant pathways, or in other words, different disease mechanisms, which are informative towards clinical outcome predictions. Indeed, we have shown that different combinations of DEG modules divided BRCA and GBM patients into subgroups that exhibit significantly different survival patterns. Since the identification of DEG modules was driven by estimates of causal relationships between SGA and DEG events, our approach provides underlying mechanistic information for each cancer subtype, and such information can potentially be used to guide future targeted therapy in a pathway-oriented fashion. This differentiates our method from previous approaches of using gene expression data to discover cancer subtypes, which usually do not provide mechanistic information.51. Specifically, since the DEG networks were constructed based on regulatory relationships between DEGs, we put more emphasis on identifying modules that connect sequences of DEGs rather than modules with a high direct correlation between any pair of DEGs. Such sequences of DEGs may represent cascades of aberrant signalling resulting from upstream perturbed genes. Two DEGs that are indirectly connected through a subsequence of other genes may still be controlled under the same regulatory network. In addition, our DEG networks were relatively dense , where classical hierarchical clustering or k-means would fail to untangle the correlations among DEGs and be unable to identify robust modules, no matter whether the correlations among DEGs were measured as co-regulation frequencies or more traditional expression profile distances . The remaining steps are identical to steps 2–6 in the standard spectral clustering algorithm18. In particular, a Laplacian matrix is computed from the optimized affinity matrix, from which the k largest eigenvectors are extracted to project the data into a k dimensional feature space. The data points are then clustered via the k-means algorithm.The spectral clustering we implemented to identify modules from the DEG networks was derived from the algorithm described by Ng, 2002k-centres of clusters. To determine the value of k , we first tried consensus spectral clustering with k = 5, 10, 15, 20, then narrowed down to the range between the two adjacent ks that gave the most stable consensus matrices, and tried each k from the range. For generating the consensus matrix of each k, a spectral clustering was repeated independently for 100 times with different random initializations. The value of k was selected such that further increasing k would result in modules that were unstable, with significant overlaps across modules on the consensus matrix. Such overlaps indicated that the data points that were finally assigned to two different modules were often clustered into the same group across independent runs. This suggests that the two modules should be merged and the k being used was too large. The module assignments used in constructing the survival features were generated by running the clustering algorithm one more time with the selected k.With the use of k-means, the clustering result partially depends on the random initialization of the 20, accessed through the Synapse repository . The experimental protocol for the METABRIC data has been approved by the University of Pittsburgh Institutional Review Board (IRB# PRO18010238). For GBM, the microarray gene expression data and clinical data were downloaded from TCGA through the Firehose browser of the Broad Institute. All computational methods applied on the data in this study were carried out in accordance with relevant guidelines and regulations of the METABRIC dataset and TCGA database. The DEG modules were obtained with TCI inferences that were produced using RNA-seq data from the TCGA database, and some DEGs were not available from the METABRIC expression data or the TCGA GBM microarray data. As a result, the number of DEGs used to compute each DEG module feature was smaller than the original number of DEGs in each module. We refer to these DEGs as the effective DEGs  were also added. For BRCA, the gene expression, clinical and survival data used were from the METABRIC projectConsensusClusterPlus52, version 1.38.0. The number of patient groups was determined using the consensus matrix and the area under the consensus cumulative distribution function curve (AUCDFC). This was done by clustering with the number of groups that varies from 2 to 15  and selecting the point at which there was no significant overlap between the resulting groups on the consensus matrix and at which further increasing the number of groups would not lead to a significant increase in the AUCDFC.Patient groups were identified using consensus clustering, with PAM clustering as the base method. The consensus clustering function was from the R package survival53 version 2.41.3. The same package also provided the functions we used for doing the log-rank test and the Cox regressions. The prediction performances of various Cox regression models were compared by computing the C-index of the model on the survival data55.The Kaplan-Meier plot of each patient group was generated using the R package All multipart figures were prepared using Adobe Photoshop CS6 version 13.0. Contrast was adjusted for Figs Supplementary FigureSupplementary Table S1-S11


Caenorhabditis elegans, the Rac proteins CED-10, RAC-2 and MIG-2 act in parallel to control axon outgrowth and guidance. Here, we have identified a single glycine residue in the CED-10/Rac1 Switch 1 region that confers a non-redundant function in axon outgrowth but not guidance. Mutation of this glycine to glutamic acid (G30E) reduces GTP binding and inhibits axon outgrowth but does not affect other canonical CED-10 functions. This demonstrates previously unappreciated domain-specific functions within the CED-10 protein. Further, we reveal that when CED-10 function is diminished, the adaptor protein NAB-1 (Neurabin) and its interacting partner SYD-1 (Rho-GAP-like protein) can act as inhibitors of axon outgrowth. Together, we reveal that specific domains and residues within Rac GTPases can confer context-dependent functions during animal development.Rac GTPases act as master switches to coordinate multiple interweaved signaling pathways. A major function for Rac GTPases is to control neurite development by influencing downstream effector molecules and pathways. In  Caenorhabditis elegans to model neurite development, we found that a single amino acid in a conserved domain of the Rac GTPase CED-10 is crucial for controlling neurite outgrowth in a partially non-redundant manner. Further, we revealed that lesions in discrete domains in the CED-10 protein lead to distinct developmental defects. Therefore, our in vivo study proposes that regulation of distinct signalling pathways through Rac GTPase protein domains can drive different developmental outcomes.Brain development requires that neurite outgrowth and guidance are precisely regulated. Previous studies have shown that molecular switch proteins called Rac GTPases perform redundant functions in controlling neurite development. Using a pair of bilateral neurons in the nematode  Caenorhabditis elegans ventral nerve cord (VNC) contains two fascicles housing multiple neurons that project axons along the length of the animal , 36rp100 alleles . As prev animals  [36]. In animals . To ask ogenesis . We founhatching . We nextgulfment . Double D-6/GULP , or the D-6/GULP , had no ced-10(rp100) mutant animals do not adversely affect apoptotic corpse engulfment. In a reciprocal experiment, we asked whether signalling components through which CED-10 controls apoptotic cell recognition, engulfment and removal are required for PVQ axon outgrowth [ced-5(tm1949) mutant animals exhibit ~35% penetrant defects in PVQ guidance (ced-10(rp100) mutation on PVQ axon outgrowth is not likely due to defective interpretations of signals from the core apoptotic corpse engulfment machinery.We have shown that the molecular pathways dysregulated in utgrowth . We examguidance , which srp100, n3246 and n1993 mutant animals  mutation on neurodevelopment compared to the n3246 and n1993 mutations, we crossed all three alleles into fluorescent reporter strains to permit visualization of neuronal development at single-neuron resolution  choice of the VD motor neurons, and in the PLM and PVM neurons  and candidate genes that encode known Rac GTPase interactors or are known to regulate cytoskeletal remodelling at the growth cone  mutation on axon outgrowth could be due to a change in affinity with interacting molecules and/or altered activity or expression of its effector molecules. To determine the pathways through which defective CED-10(G30E) protein may cause PVQ outgrowth defects, we performed single and double mutant analysis with wth cone .ced-10(rp100) PVQ defects is not affected by the rin-1(gk431) mutation (mig-10(ct41) null mutant animals exhibit minimal PVQ axon outgrowth defects (2%), albeit with highly penetrant guidance defects (75%), and does not affect the PVQ outgrowth penetrance of ced-10(rp100) mutant animals (C. elegans abLIM homolog UNC-115 is an actin modulatory protein that functions to control growth cone filopodia formation [unc-115(ky275) null mutant animals exhibit a low penetrant axon guidance defect (unc-115(ky275); ced-10(rp100) animals is additive when compared to either single mutant suggesting that these factors regulate PVQ guidance in parallel  nor max-2(ok1904) mutants caused PVQ outgrowth defects. However, max-2(ok1904) animals do exhibit PVQ axon guidance defects, consistent with the finding that MAX-2 can regulate axon guidance independently of Rac proteins [ced-10(rp100) PVQ axon outgrowth defects. In contrast, we found that two independent alleles of pak-1 suppress the PVQ outgrowth defects of ced-10(rp100) mutant animals, without affecting the penetrance of PVQ guidance defects (ced-10(rp100)-induced defects are suppressed. Alternatively, our observations could mean that CED-10(G30E) protein specifically alters PAK-1-regulated outgrowth.Next, we analysed the function of the p21-activated kinases (PAKs), which are known downstream Rac GTPase effectors that are important for controlling cytoskeletal dynamics. Dimerized PAKs interact with GTP-bound Rac proteins, which relieves PAK self-inhibition and permit kinase domain activation . We anal-2 genes . We founproteins .Furtherm defects . One intced-10(rp100) PVQ axon outgrowth defects, we performed a forward genetic suppressor screen (ced-10(rp100) animals exhibit PVQ outgrowth defects when cultivated at 25°C (ced-10(rp100); oyIs14 strain with ethyl methanesulfonate and screened for reduced PVQ outgrowth defects in the progeny of 1000 F2 animals cultivated at 25°C. Of the four independent alleles that significantly suppress ced-10(rp100) PVQ outgrowth defects, we focus on rp117, a recessive allele that reduces ced-10(rp100) PVQ outgrowth defects at 20°C from ~35% to ~10%  PVQ outgrowth defects (nab-1 does not suppress PVQ axon guidance defects (nab-1 is dispensable for correct PVQ development in a wild type background (ced-10(rp100) mutant background, we expressed nab-1 cDNA using a PVQ specific promoter (nab-1 cDNA reversed the suppression of PVQ axon outgrowth defects observed in the ced-10(rp100); nab-1(ok943) double mutant  axon outgrowth defects. To test this hypothesis, we generated compound mutants between ced-10(rp100) and two, independent, syd-1 alleles (ju82 and tm6234). We found that both syd-1 alleles suppress ced-10(rp100) PVQ outgrowth defects (nab-1 and syd-1 did not further suppress ced-10(rp100) PVQ outgrowth defects, suggesting that these genes act in the same pathway to regulate neuronal outgrowth  with putative protein and RNA binding function –56. Prevus actin . SYD-1 ius actin . We hypo defects . Furtherutgrowth .ced-10(rp100); mig-2(mu28) double mutant, which exhibits highly penetrant defects in PVQ axon outgrowth (ced-10(rp100); mig-2(mu28) animals. However, we found that PVQ axon outgrowth defects of ced-10(rp100); mig-2(mu28) animals are suppressed when nab-1 or syd-1 are absent (mig-2(mu28) is a null allele, these data suggest that NAB-1 and SYD-1 can regulate the activity of alternative GTPases, or may directly regulate CED-10 to control PVQ axon outgrowth.Our data suggest that NAB-1 and SYD-1 are required to suppress Rac GTPase activity during PVQ axon outgrowth. We have shown that CED-10 and MIG-2 act redundantly in PVQ axon outgrowth , and a putgrowth . If NAB-e absent . As mig-C. elegans CED-10/Rac1 that is crucial for regulating neuronal development  mutant animals exhibit similar PVQ axon outgrowth defects. In addition, PVQ axon outgrowth defects caused by ced-10(rp100) are fully recessive and are rescued by expressing wild type ced-10 cDNA. We also demonstrate that animals harboring a known Rac1 hyperactive mutation (proline to serine substitution at position 29) in CED-10 did not exhibit PVQ axon outgrowth defects. Most importantly, using in vitro GTP activation assays we found that Rac1(G30E) is GTP activated ~50% less than wild type Rac1. The reduced affinity of CED-10(G30E) for GTP may result in diminished and/or atypical interactions between CED-10(G30E) and unknown effector molecule(s), thereby causing the PVQ axon outgrowth phenotype.Our multiple lines of evidence show that the G30E substitution reduces CED-10 function. We have shown that We found that the CED-10(G30E) mutant protein causes profound effects on PVQ outgrowth. However, unlike other CED-10 mutant proteins, CED-10(G30E) does not cause defects in apoptotic corpse engulfment or fecundity. This suggests that defects in cell behaviour caused by the CED-10(G30E) protein specifically affects nervous system development. It could also suggest that the activity of CED-10(G30E) is not sufficiently reduced to impinge upon apoptotic corpse engulfment or generation of progeny. Focusing our analysis on the nervous system, we found that the neuronal guidance defects caused by CED-10(G30E) are similar to another mutation affecting the Switch 2 region, CED-10(G60R). However, we only found PVQ axon outgrowth defects in CED-10(G30E) animals. To our knowledge, this is the first report where different regions of Rac GTPases have been shown to differentially regulate axon outgrowth and guidance. Our data therefore suggest that PVQ outgrowth is especially sensitive to CED-10(G30E)-defective signaling and is an ideal model to delineate previously unknown functions for the Switch 1 region of Rac GTPases in regulating cell behaviour. Further analysis of the actin cytoskeletal architecture in the PVQ neurons during embryogenesis—the time of PVQ outgrowth, is unfortunately not presently feasible, as the tools to observe PVQ-specific reporter proteins during embryogenesis are unavailable.C. elegans Neurabin homolog, suppresses ced-10(rp100) PVQ axon outgrowth defects. In C. elegans, NAB-1 regulates synaptogenesis and synaptic function by binding to F-actin and recruiting the synaptic active zone proteins SYD-1 (RhoGAP) and SYD-2 (liprin-α) , 54. In mammals, Neurabin I was originally shown to promote neurite formation in primary rat hippocampal neurons [We performed a genetic suppressor screen to identify the regulators controlling CED-10-directed PVQ axon outgrowth. This approach revealed that loss of NAB-1, the  neurons . Further neurons . Neurabi neurons . Subsequ neurons . Support neurons , 64. The neurons . These d neurons .C. elegans. Rac GTPase activation status is central for regulating downstream effector pathways, and we found that the scaffolding protein NAB-1 and RhoGAP SYD-1 likely enhance CED-10 activity and therefore modify CED-10 output. Intriguingly, we found that although reduced NAB-1 and SYD-1 function promoted axon outgrowth, it did not restore PVQ axon guidance. This supports the hypothesis that there are intracellular mechanisms in growth cones that are specifically regulated by effector molecule binding to Rac GTPases, which differentially promote/inhibit axon outgrowth and guidance. We propose that identifying the precise effector molecules and pathways controlled by CED-10 under different levels of activation will uncover novel insights into how correct axon outgrowth and guidance is achieved.Taken together, our work reveals that a conserved amino acid (glycine 30) in the CED-10/Rac1 protein is important for the activation status of this Rac GTPase and for correct axon outgrowth of the PVQ neurons in C. elegans strains were maintained at 20°C on NGM plates seeded with Escherichia coli OP50 bacteria, unless otherwise stated [All e stated . StrainsoyIs14, hdIs26, rpEx1640; Mechanosensory neurons:  zdIs5; HSNs: rpEx6; PDEs: IqIs2; PVPs: hdIs26; AVGs: otIs182; D-type motor neurons: oxIs12; AQR: rpIs8. All neuronal scoring was repeated in triplicates on independent days, n = 75, except for oxIs12 scoring, n = 35.Neurons were scored, blinded to phenotype, in L4/young adult stages, unless otherwise stated. Scoring criteria for each neuron is detailed in nDf67(mir-51 mir-53 deletion); oyIs14 [nDf67 deletion, from which we singled animals and genotyped for loss of the nDf67 deletion. These animals still exhibited the PVQ outgrowth phenotype. We named the mutant allele rp100 and used the one-step whole-genome sequencing and SNP mapping strategy to map the genetic lesion [rp100; oyIs14 hermaphrodites. Ten F1 progeny carrying the oyIs14 transgene were picked to individual plates and allowed to self-fertilize. F2 progeny carrying the oyIs14 transgene were picked to 250 individual plates and allowed to self-fertilize. Approximately 20 F3 progeny from each of the 250 F2 plates were scored for PVQ axon outgrowth defects. 47 plates were homozygous for the phenotype causing mutation, based on the presence of PVQ outgrowth defects. Progeny of these animals were pooled and DNA was isolated. Pooled genomic DNA was sequenced using Ilumina sequencing and the resultant sequencing data was analyzed using the Galaxy platform. Our mapping identified a single lesion (GGA-GAA) at base-pair position 89 within exon 1 of the ced-10 gene, which was independently confirmed by Sanger sequencing.We initially observed PVQ axon outgrowth and guidance defects in the following strain—; oyIs14 . After tc lesion . Males oced-10(rp100); oyIs14 animals were induced with ethyl methanesulfonate (EMS) following the modified version of a previously described protocol [ced-10(rp100), screening for suppressor mutations was conducted at 25°C. The germlines of ced-10(rp100); oyls14 animals (L4 stage) were mutagenised using 50mM EMS (Sigma) in M9 buffer  for 4 hours (room temperature), after which they were transferred to standard OP50 NGM plates for 72 hours (20°C). F1s were allowed to self-fertilise on individual plates (20°C). Four F2s (L4 stage) were randomly picked from each F1 and separated into individual plates for incubation at 25°C. F3 populations with PVQ outgrowth defects lower than 20% (n = 50) were selected and retested in subsequent generations for heritability of suppression.Random mutations in protocol . Since iced-10(rp100); oyls14 and 200 recombinant F2s from each cross were separated into individual plates for incubation at 25°C. Recombinant F2s homozygous for the suppressor mutation were identified by scoring for suppression of PVQ outgrowth defects of F3 offspring. 40 F2 plates exhibited the suppressor phenotype and worms were washed off with M9 buffer and pooled for DNA extraction with Gentra Puregene kit (Qiagen). Paired-end whole genome sequencing (WGS) with Illumina NextSeq500 was performed and the causative genetic lesion identified using Mutation Identification in Model Organism Genomes (MiMoDd) (version 0.1.7.3).Suppressor gene identification was carried out using whole genome sequencing and bulk-segregant mapping . CandidaPlasmid inserts were confirmed using Sanger sequencing prior to use.sra-6prom::ced-10cDNAbRJP273 ced-10 cDNA isoform b was amplified from an oligodT amplified cDNA library using the oligos 5’-ttggctagcgtcgacggtacatgcaagcgatcaaatgtg-3’ and 5’-agatatcaataccatggtacttagagcaccgtacactt-3’ and ligated into a sra-6prom plasmid with a pPD49.26 backbone using KpnI.rgef-1prom::ced-10cDNAbRJP272 ced-10 cDNAb from RJP273 was ligated downstream of a rgef-1prom in a pPD49.26 backbone using NheI-SpeI.sra-6prom::GFPRJP296 sra-6prom plasmid with a pPD49.26 backbone using NheI-SpeI.Sequence encoding GFP was ligated into a sra-6prom::mCherryRJP297 sra-6prom plasmid with a pPD49.26 backbone using NheI-SpeI.Sequence encoding mCherry protein was ligated into a npr-11prom::nab-1RJP370 nab-1 cDNA was amplified from an oligodT amplified cDNA library using the oligos 5’- gctagcatgacaacggcttccgagc -3’ and 5’- ggtacctcacatgggaattgtgtgtgc-3’ and ligated into a npr-11prom plasmid with a pPD49.26 backbone using NheI-KpnI.3. Examination and imaging of neurons was performed using an automated fluorescence microscope  and ZEN software (version 3.1).Animals were mounted on 5% agarose pads and immobilized using 50mM NaNced-10(rp100); oyIs14. The WRM0639dH09 fosmid spanning the entire ced-10 locus was injected at 1ng/μl. For pan-neuronal rescue, RJP272 (rgef-1prom::ced-10cDNAb) was injected at 10ng/μl. For cell-autonomous rescue, RJP273 (sra-6prom::ced-10cDNAb) was injected at 50ng/μl and RJP370 npr-11prom::nab-1 at 10ng/μl. RJP370 (npr-11prom::nab-1) was injected into nab-1(ok943); ced-10(rp100); oyIs14 animals at 20ng/μl. myo-2prom::mCherry was injected at 5ng/μl as a co-injection marker in all transgenic animals.Transgenic animals were generated as previously described . Rescue rgef-1prom::ced-10cDNAb) at 10ng/μl, RJP297 (sra-6prom::mCherry) at 10ng/μl and myo-2prom::mCherry at 5ng/μl into the ced-10(rp100); oyIs14 strain. A transgenic line was selected that exhibited rescue of the PVQ axon outgrowth and guidance defects. Transgenic animals from this line were then scored for phenotypic rescue of the PVQ defects in the presence and absence of the rescuing extrachromosomal array in the PVQ neurons by detection of mCherry fluorescence.For mosaic analysis, transgenic animals were generated by injecting RJP272 .1-177 was obtained from Christina Lucato [pGEX-4T-1-TEV-Rac1a Lucato . Point mEscherichia coli BL21(DE3) CodonPlus cells and GST-Rac1 protein expression was induced by IPTG at 18°C overnight. Cells were pelleted and resuspended in lysis buffer . Cells were lysed by sonication and cell debris was cleared by centrifugation. Soluble lysate was filtered at 0.8 μm. GST-Affinity purification was carried out with glutathione-Sepharose 4B resin  at 4°C for 90 min. The GST tag was cleaved with overnight incubation with TEV protease. Rac1 was eluted and further purified with size-exclusion chromatography on a HiLoad Superdex 75 16/60 column , equilibrated in SEC buffer .pGEX-4T-1-TEV-Rac1 wild type and mutant plasmids were transformed into 2 . EDTA and excess GDP is removed from GDP-loaded Rac1 with PD-10 desalting columns and equilibrated with 20 mM Tris, pH 8.0, 150 mM NaCl, 10 mM MgCl2, 2mM DTT. 30 μM Rac1-GDP was incubated with 500 μM GTPγS with or without 15 mM EDTA for 1 hour at room temperature and the reaction was terminated by adding 20 mM MgCl2 . 5 μg Rac1-nucleotide complexes were allowed to bind with 5 μg PAK-PDB beads (Cytoskeleton) in 500 μl of pulldown buffer  Igepal CA-630) for 1 hour at 4°C. Beads were washed three times with cold pulldown buffer. Active Rac1 bound to PAK beads were eluted in Lithium Dodecyl Sulfate loading buffer and subjected to SDS-PAGE. Eluted Rac1 protein was detected by western blotting and anti-Rac1 mouse monoclonal antibody (Cytoskeleton). Western quantification was carried out with ImageJ with intensities normalised to loading controls. Method adapted from [Purified wild type and mutant Rac1 proteins (500 μM) in SEC buffer was preloaded with 10 mM GDP and 5 mM EDTA, for 1 hour at room temperature. The reaction was terminated by adding 10 mM MgClted from .Statistical analysis was carried out using Graphpad Prism 7 using one-way ANOVA with Tukey’s Multiple Comparison Test or t test, where applicable. Values are expressed as mean ±S.D. Values <0.05 were considered statistical significant.S1 Figced-10(rp100) rescue and mosaic analysis.Homo sapiens Rac1 (HsRac1) and C. elegans CED-10 (CeCED-10). The ced-10 point mutants used in this study are marked with an asterisk: rp100 (G30E - Switch 1 region), n3246 (G60R - Switch 2 region) and n1993 (V190G - membrane targeting region).(A) Alignment of the amino acid sequences of ced-10(rp100) animals with ced-10 cDNA (ced-10(+)). Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, comparing wild type and rp100 animals; #<0.0001 and +<0.006 and comparing the three transgenic rescue lines to ced-10(rp100). n.s. not statistically significant from rp100 animals. n = 90 per strain, each dot represents independent scoring replicates.(B) Quantification of pan-neuronal and PVQ-specific transgenic rescue of total PVQ defects of Prgef-1::ced-10(+), Psra-6::mCherry and Pmyo-2::mCherry plasmids was expressed in ced-10(rp100); oyIs14 animals. PVQ outgrowth and guidance was scored in animals in which the array was absent , present in the PVQs  or missing from the PVQs . Number of animals scored is shown in parentheses.(C) An unstable transgenic array containing (PDF)Click here for additional data file.S2 Figced-10 allelesgenetic analysis of .ced-10 mutant homozygotes, heterozygotes and transheterozygotes. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, n.s. not significantly different to wild type. n = 90 per strain, each dot represents independent scoring replicates.(A) Quantification of total PVQ defects (outgrowth and guidance) in wild type, ced-10 mutant homozygotes, heterozygotes and transheterozygotes. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, n.s. not significantly different to wild type. n = 90 per strain, each dot represents independent scoring replicates.(B) Quantification of PVQ outgrowth defects in wild type, ced-10(tm597) L1 escapers. Data are expressed as mean ±SD and statistical significance was assessed using t test. n = 9 per strain, each dot represents independent scoring replicates.(C) Quantification of PVQ outgrowth defects in wild type L1 larvae and (PDF)Click here for additional data file.S3 Figapoptotic pathway function.(A) Images of the heads of freshly hatched L1 animals. Persistent apoptotic cell corpses are marked with white arrows.ced-10 mutant animals. The number of persistent cell corpses was counted at the bean stage of embryogenesis. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, n.s., not statistically significant from wild type. n = 21, from three independent scorings.(B) Quantification of apoptotic corpse engulfment in ced-10(rp100) mutant animals. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, *<0.01, n.s., not statistically significant from wild type. n = 15, from three independent scorings.(C) Quantification of apoptotic corpse engulfment in wild type and ced-10(rp100) L1 larvae combined with mutations in CED-6/GULP and CED-12/ELMO. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. n.s. not statistically significant comparing ced-6 and ced-12 single mutants combined with ced-10(rp100). n = 17, from three independent scorings.(D) Quantification of persistent cell corpses present in the head of freshly hatched (E) Quantification of PVQ outgrowth defects in wild type and mutants of apoptotic pathway genes. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, n.s. not significantly different to wild type. n = 75 per strain, each dot represents independent scoring replicates.(F) Quantification of total PVQ defects (outgrowth and guidance) in wild type and mutations in apoptotic pathway genes. Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, n.s. not significantly different to wild type. n = 75 per strain, each dot represents independent scoring replicates.(PDF)Click here for additional data file.S4 Figgenetic analysis of NAB-1 function in PVQ development. Quantification of total PVQ defects (outgrowth and guidance) in wild type, ced-10(rp100) and compound mutants between ced-10(rp100) and nab-1 alleles . Data are expressed as mean ±SD and statistical significance was assessed using one-way ANOVA, with Tukey’s multiple comparison test. ****<0.0001, comparing wild type and rp100 animals, n.s. not statistically significant when compared to either wild type (for single mutants) or ced-10(rp100) for compound mutants. n = 90 per strain, each dot represents independent scoring replicates.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA . The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways. Cancer is caused by the interactions of multiple genes and pathways. Pathway analysis may help to understand the status of cancer and suggest customized anticancer therapies. Wang et al.3 classify pathway analysis methods into four main categories: overrepresentation analysis (ORA), functional class scoring (FCS), pathway topology (PT) - Based and network topology (NT) - Based.Cancer etiology and progression is currently understood to be driven primarily by molecular and genetic mechanisms4 approaches assess whether the number of genes beyond an arbitrary threshold is significantly over- or under-represented in a pathway just by chance. Unlike ORA, FCS5 methods take into consideration all available molecular measurements for pathway analysis, such as GSEA6, ANCOVA7, etc. PT-Based8 methods employ pathway topology between genes in signaling pathways to find which pathway is most impacted by a given phenotype. Moreover, the interaction databases, such as HPRD9, FunCoup10, STRING11, are also available. So, NT-Based3 methods extract interactions between genes from interaction databases or literature to compute pathway-level statistics.ORAet al.12 embed original pathways within large-scale networks and demonstrate the crosstalk between them. Original pathways are extended by mapping genes of original pathways onto the network of biomolecules. The first neighbors of these genes are considered as new components of the original pathways. Glaab et al.13 present a methodology for extending original pathways by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. However, these methods only consider network topologies and ignore edge weights of large-scale networks when extending pathways. Zhang et al.14 calculated the weights of a gene network through integrating DNA methylation and gene expression data to identify disease-associated gene modules. However, the biological roles of the gene modules discovered using the method are not clear. Paradigm16 integrates diverse high-throughput genomics information with a pathway structure to identify significant pathways. It has a limitation to extract different types of biological entities in the context of biological knowledge. And, this method only employs the pathway topology itself. Hence, how to combine information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify altered pathways which have significant changes in different tissues, such as tumor and normal tissues.Recent functional genomic experiments have found a large number of interactions between intra- and inter-pathways, suggesting more complex relationships between biological pathways than in their traditional representations. Therefore, it is necessary to embed original pathways into many large-scale networks to analyze pathways. Lu 17. Hence we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to calculate edge weights of the large-scale network for each phenotype. Then, each pathway is extended by adding important neighboring genes based on the limited kWalks algorithm18 in weighted phenotype-specific networks. The pathway extended under different phenotypes is united as a final pathway gene list. Finally, by inputting the gene lists of extended pathways into the classical gene set analysis (ORA and FCS), we identify altered pathways which are correlated well with the corresponding cancer. The overview of our method is shown in Fig. DNA methylation is known to be associated with gene transcription by interfering with DNA-binding proteinshttp://ophid.utoronto.ca/ophidv2.204/downloads.jsp). Gene expression and DNA methylation data are obtained from TCGA . In this study, we have only chose samples that contain both gene expression and methylation data. According to data providers, all methylation data are from Illumina Human Methylation 450k Chip, whereas all gene expression data are downloaded from Agilent G4502A or Illumina HiSeq platform. BRCA (Breast Invasive Carcinoma) includes 33 cancer samples with DNA methylation and gene expression data, and 37 normal tissue samples. LUAD (Lung Adenocarcinoma) dataset consists of 69 samples . COAD (Colon Adenocarcinoma) data have 26 cancer samples with DNA methylation and gene expression data and 16 normal tissue samples). Gene expression data of the LUAD and COAD produced by Illumina HiSeq are added a value of 1 (to avoid zeros) and then log2-transformed. Gene sets of biological pathways are from the ConsensusPathDB website. A total of 281 KEGG pathways are obtained and further analyzed in the subsequent experiment.The PPI(Protein-Protein Interaction) network (version 2.9) was downloaded from the Interologous Interaction Database (I2D) website  and SCCA through integrating DNA methylation and gene expression data. At first, we do not set the cut-off of the gene expression and DNA methylation and treat each gene equally when building the weighted gene-gene interaction network. When calculating the weight of a gene pair in the network, if one of the two genes does not have the corresponding expression and methylation values, the edge is deleted, otherwise retained. Each gene contains multiple methylated CpG loci, and there is a general correlation between these neighboring CpG loci. In this study, PCA is used for dimensionality reduction of CpG loci for each gene firstly. Then, the selected principal components of CpG loci and gene expression are merged as the matrix of a gene. Finally, SCCA is used to calculate the edge weights of gene pairs in the network based on the principal components of CpG loci and gene expression values see Fig. .Figure 21 and ||·||2 are L1 norm and L2 norm, respectively. c1 and c2 are parameters to regulate the amount of shrinkage and restricted to ranges WXY is calculated using PMA which is available as a Bioconductor package19.Let entclass1pt{minima18 in gene-gene interaction network and the importance neighboring genes are added in the pathway. In the limited kWalks algorithm, the relevance of an edge and a node in relation to the pathway-sets is evaluated by the expected times random walk passes starting from one gene to any of the others. In the interpretation of a graph as a Markov chain, each gene represents a state, and the probability of transition from state i to j is given byWij is edge weight of gene i - gene j. More details of the mathematics are available in ref.20. Finally, we extract two extended pathways genes from two weighted phenotype-specific networks, respectively. Two extended pathways genes under different phenotypes are united as an extended pathway gene list.We construct the weighted gene-gene interaction networks for different phenotype , as shown in Fig. To illustrate the benefits of our extended pathways, we use ORA and GSEA to analyse gene sets included in the extended pathways and identify the altered pathways which are correlated well with the corresponding cancer. In this paper, for convenience they will be referred to as EP-ORA (Extended Pathway ORA) and EP-GSEA (Extended Pathway GSEA).21.Briefly, ORA methods compare sets of genes annotated to pathways and to a list of those genes that are significantly deferentially expressed (DE) between two phenotypes. Then a confidence value is calculated using statistical methods. Here, we calculate a P-value using the hypergeometric distribution.6 is an FCS-type method that determines whether a priori defined set of genes shows statistically significant, concordant differences between two biological states, which uses all available molecular measurements for pathway analysis. GSEA works as follows:Sort genes by signal-to-noise ratio;Calculate enrichment scores;Permute 1000 phenotype labels for significance.Another approach, GSEA22. The pathway neighboring genes may play important roles in the regulation of disease-related pathways. The inclusion of important neighboring genes will enable us to understand cancer mechanisms with models of pathway activities. One hypothesis of the proposed method is that the genetic interactions are variables between controls and cases which is responsible for different phenotypes varying in cancer. Hence, two weighted gene-gene interaction networks are then achieved based on case samples and control samples, respectively. All genes that interact with the pathway contribute to the regulation of the pathway. So, genes of two extended pathways under different phenotypes are eventually united as a final extended pathway gene set.In general, functionally linked interacting genes have a significantly higher level of coherence in biological systemsTo test the effectiveness of the proposed method, we first take BRCA dataset for a comparative evaluation. As shown in Fig. The extended p53 signaling pathway is illustrated in Fig. 23.One of the important applications of pathway analysis is to identify altered pathways which are correlated well with the corresponding cancer. Here, we firstly take BRCA dataset for a comparative evaluation. We apply ORA and EP-ORA to this dataset with the BH corrected P-value. Using a P-value cutoff of 0.05, ORA and EP-ORA result in picking 6 and 18 pathways as significant, respectively  and original pathways with an FDR cutoff of 25%. However, no pathway is identified .et al.26 reported that bile acid receptor accelerates to the lung cancer process induced by lung fibroblast-tumor cells interaction, with high activation of phosphorylated STAT3 and alteration of cytokine secretion. Compared with GSEA, EP-GSEA identifies more pathways which are closely related to lung cancer (FDR ≤ 25%). Interestingly, the non-small cell lung cancer pathway is only identified by EP-GSEA.The results of lung adenocarcinoma data (LUAD) are shown in Tables 27 to be inversely associated with colon cancer is ranked 2 by ORA, but ranked 53 by EP-ORA, we apply ORA and EP-ORA to COAD dataset .We present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend classical pathways. Our method can effectively identify altered pathways which are correlated well with the corresponding cancer by inputting the gene lists of extended pathways into the classical gene set analysis (ORA and FCS) on three datasets . By applying the method to the breast cancer dataset, we demonstrate the method’s potential to identify breast cancer-related pathways. The analysis of colorectal cancer and lung adenocarcinoma confirm the proposed method’s ability to correctly identify cancer-related pathways in different cancer datasets. This suggests that the integration of DNA methylation and gene expression through a known gene interactions network is effective in pathway analysis. In the future, we will employ more datasets to assess the validity of our method. Readers can download our code from the website (Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6


Differentially expressed genes (DEGs) were identified between non-diabetic and diabetic samples in murine models, and non-progressive and progressive human samples using a unified analysis pipeline. A transcriptional network for each DEG set was constructed based on literature-derived gene-gene interaction information. Seven pairwise human-vs-murine comparisons using a network-comparison program resulted in shared sub-networks including 46 to 396 genes, which were further merged into a single network of 688 genes. Pathway and centrality analyses revealed highly connected genes and pathways including LXR/RXR activation, adipogenesis, glucocorticoid receptor signalling, and multiple cytokine and chemokine pathways. Our systems biology approach identified highly conserved pathways across human and murine models that are likely to play a role in DPN pathogenesis and provide new possible mechanism-based targets for DPN therapy.Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes. In this study, we employed a systems biology approach to identify DPN-related transcriptional pathways conserved across human and various murine models. Eight microarray datasets on peripheral nerve samples from murine models of type 1 (streptozotocin-treated) and type 2 ( The most common microvascular complication of diabetes is diabetic peripheral neuropathy (DPN) which occurs in approximately 60% of patients and is the leading cause of non-traumatic lower-limb amputations3. DPN is characterized by distal to proximal degeneration of peripheral nerves which results in symptoms such as numbness, pain, and weakness4. Other than glucose control, there are no disease-modifying treatments for DPN. Understanding DPN pathology and identifying the underlying mechanisms of peripheral nerve degeneration are therefore critical to the development of new mechanism-based therapies for DPN.According to the United States Centres for Disease Control and Prevention (CDC), more than 29 million Americans, over 9% of the United States population, are living with diabetes and another 86 million have prediabetes9 and human subjects11 with diabetes. Bioinformatics analyses of these high-throughput datasets identified numerous genes in human and murine peripheral nerves that are significantly dysregulated by diabetes. Suggested mechanisms of injury such as inflammation, oxidative stress, lipid and carbohydrate metabolism, regulation of axonogenesis, mitochondrion, and peroxisome proliferator-activated receptor (PPAR) signalling were also reported during diabetes onset and progression10.Over the past decade, with the advent of high-throughput gene expression profiling assays such as microarrays and RNA-Seq, we and others have examined genome-wide gene expression changes from the peripheral nerve tissues of various diabetic murine modelsYet, one limitation of the previous studies, including ours, is that the analyses didn’t account for the differences in species, strains, procedure of diabetes induction, and diabetes duration. Another critical issue that hasn’t been addressed thus far is the identification of common injurious pathways and networks conserved across various mouse models of diabetes as well as between mouse and human. In this study, we reanalysed previously published DPN-related microarray datasets from human and multiple murine models using a unified analysis pipeline. Compared with the existing literature, this study provides a unique opportunity to uncover a possible common mechanism of injury shared across DPN stages, types of diabetes, and species. Such mechanisms could unravel new important therapeutic targets to treat DPN.http://hurlab.med.und.edu/DNMKB/). The data from the type 1 diabetes mellitus (T1DM) model were originally generated from male DBA/2J mice treated with streptozotocin (STZ) at 10 weeks that were terminated at 34 weeks9. The two type 2 diabetes mellitus (T2DM) models included BKS.Cg-Leprdb/db (db/db) mice and BTBR. Cg-Lepob/ob (ob/ob) mice. The male db/db mice were 8, 16, and 24 weeks12, while male ob/ob mice were 5 and 13 weeks of age6. A female ob/ob dataset was also available from 26 weeks old BTBR.Cg-Lepob/ob mice13. Each dataset was originally generated using sciatic nerve samples on the Affymetrix Mouse Genome 430 2.0 array platform. As previously reported, each model displayed the features typical of diabetes by the termination of their respective studies as well as the hallmarks of DPN13. The human sural nerve data used was generated using the Affymetrix Human Genome U133 Plus 2.0 array platform7. As previously reported these samples were evaluated for features of DPN and were separated into progressive and non-progressive groups based on the myelinated fibre density lost over a 52 week period7.All datasets used were gathered from the Diabetic Neuropathy Microarray Knowledge-Base  to identify the differentially expressed genes (DEGs) between groups. The DEG lists identified from each murine model were converted to the human orthologue equivalent when possible using the Genomatix annotation orthologue database. These DEG lists of human gene IDs were then compared across datasets to identify the conserved DEGs between the different murine models and human. Each DEG set was also analysed using Ingenuity Pathway Analysis  from Qiagen  and the identified pathways were compared across models and species to identify the commonly disrupted pathways. To identify gene networks common between models, networks were generated from these DEG datasets using SciMiner14. The networks generated were analysed for common network nodes and disrupted pathways using IPA.Figure https://www.bioconductor.org/) and their Affymetrix QC packing in R . All data passing our quality threshold were then analysed using ChipInspector and up-to-date gene annotation. Expression signals from the microarray image files were analysed at the single probe level. Significant transcripts were defined using a minimum of five significant probes and a false discovery rate of <1% by the Significance Analysis of Microarrays algorithm using exhaustive comparisons between control and diabetic mice. Eight datasets of DEGs were generated based on significant transcripts between control and diabetic groups for each murine model  as well as between progressive and non-progressive groups for the human dataset13.Transcriptomic data generated from Affymetrix GeneChips were analysed using BioConductor 15, an open-source platform for visualizing complex networks, was used to visualize SciMiner-generated transcriptional networks.Each DEG set was used to generate transcriptional networks based on gene-gene associations that were identified from biomedical literature using SciMiner16. TALE compares network structure and extracts overlapping conserved relationships between two networks. The mismatch parameter used allows 10% mismatch when generating seed gene nodes and extended networks as in our previous studies18. Conserved nodes across networks were then examined and analysed for overlapping pathways using IPA.Each mouse DEG network was compared to the human DEG network in a pairwise manner, using a Tool for Approximate Subgraph Matching of Large Queries Efficiently (TALE)IPA was used to identify enriched pathways within each of the eight DEG sets as well as the seven TALE generated network comparison datasets (comparing each mouse DEG set to our human DEG set). These pathways for the eight DEG sets were compared to identify conserved disrupted pathways that may indicate an underlying cause to DPN. The disrupted pathways observed in our TALE datasets were also compared in order to identify a possible central pathway based on nodes and subnetworks generated from our transcriptional network comparison. The background list of genes used for this analysis was the Affymetrix Human Genome U133 Plus 2.0 Array.sna’, tools for social network analysis, in R (https://CRAN.R-project.org/package=sna), four centrality metrics  were computed to identify the most important nodes  in the merged transcriptional network. These four different centrality metrics measure different aspects of node characteristics20.Eight TALE networks were combined using the merge function in Cytoscape. Edges supported by less than three citations according to SciMiner were removed from the network. The merged human-mouse-conserved transcriptional network was examined to identify the most central genes in the network. Using package ‘i.e., directly connected to the given node). The more connections a node has, the more central it is, based on degree centrality. In eigenvector centrality, a node contributes to the centrality of another node proportionally to its own centrality. A node is more central, if it is connected to many central nodes. The other two metrics (closeness and betweenness) are dependent on the position of a node in the network. Closeness centrality is based on the distance of a node to the other nodes in the network. The closer a node is to the other nodes, the more important it is considered to be. Betweenness centrality is based on the number of shortest paths connecting two nodes that pass over the given node. A node is more central, if it acts like a bridge in the network, i.e., lies on many shortest paths. In the current study, we defined the top 10 or 50 most central genes, belonging to the ranks of the genes using each metric. These gene sets were further examined for their enriched biological functions using IPA.Briefly, the degree centrality is the number of nodes that are its first neighbours  were significantly heavier and displayed higher levels of fasting glucose levels and glycosylated hemoglobin, when compared with age-matched non-diabetic controls . Triglyceride levels were significantly increased in T2DM mice. STZ-induced T1DM mice had significantly reduced body weight and higher fasting blood glucose and glycosylated hemoglobin levels relative to non-diabetic controls. Motor and sensory nerve conduction velocities (NCVs) were significantly lower in diabetic mice at all stages of diabetes. Intra-epidermal nerve fibre density (IENFD) was significantly decreased in ob/ob mice at 9 and 13 weeks compared with age-matched control littermates. Similar changes were observed in the db/db mouse model at 16 and 24 weeks of age. Both IENFD loss and reduced NCVs confirm the development of DPN in murine models of disease21. In the human subjects, there was a change in myelin fibre density (MFD) over the course of the 52-week study between subjects with progressive versus non-progressive neuropathy10. However, other factors, including gender, age, insulin treatment, triglyceride levels, glycosylated haemoglobin and body mass index (BMI), were not significantly different between the two groups.Gene expression profiles were generated using eight published datasets from sciatic nerve samples from both T1DM and T2DM murine models with DPN as well as human sural nerves from patients with T1DM and T2DM10. Across all eight datasets, over 11,000 genes were identified with at least 2,100 being shared across at least 3 datasets. Most murine models displayed a similar expression pattern while our T1DM model (STZ-treated) showed a distinct pattern , macrophage scavenger receptor 1 (MSR1)25, and matrix metallopeptidase 12 (MMP12)26, which have been implicated in the pathogenesis of DPN. Supplementary Fig. 28.ChipInspector was used to reanalyse each dataset and identified between 438–5,757 DEGs within each dataset Table . Gene ex14. These networks were then compared in a pairwise manner between murine and human datasets using TALE with a 10% mismatch parameter to limit the shared network16. Networks ranging from 46–396 genes were identified as shared between each murine set of DEGs and the progressive human set of DEGs , a serine peptidase that modulates the levels of incretin hormones, major regulators of glucose homeostasisCytoscape was then used to visualize each shared TALE network. Networks were merged using the merge network feature in Cytoscape in order to identify the most connected DEGs Fig. . Each inThe genes in the shared transcriptional networks between human and each murine model were analysed for pathway enrichment using IPA. Over 380 pathways were found to be significantly enriched and the top pathways shared across each network are shown in Fig. sna package in R, this merged network was analysed to identify the most central and influential genes within the network. The four measures of centrality used included closeness, betweenness, degree, and eigenvector values. Table 34.Seven human and murine shared networks were merged into a single network, consisting of 688 genes that interacted with up to 304 other genes in the network Fig. . Using t10 as a measure of significance; the ratio represents the proportion of the 14 central genes to all the genes involved in the canonical pathway. Ratio values in this case are expected to be low since our input gene list was only the 14 genes identified as most central within the network. The most significant pathways include HMGB1 signalling and Glucocorticoid receptor signalling.The 14 most central genes were also used as input to IPA in order to identify enriched pathways represented by these influential genes. The top 20 canonical pathways are shown in Table 35, a network clustering algorithm, was used to identify sub-networks or clusters that are highly interconnected. These clusters shared common functional themes, and the largest cluster of canonical pathways was associated with immune response and inflammation  and extracellular matrix homeostasis , that have been highlighted in our previous study, were also altered in the current setting7. Consistent with previous reports, transcriptional changes associated with estrogen signalling were observed, as sex-specific risk levels for diabetic complications have been well documented in human36 and mouse37 models.Many of the pathways that were dysregulated in our previous reports were similarly dysregulated when compared across the seven murine models and human samples, including pathways associated with immune system, cellular development and cellular survival28, we observed transcriptomic and functional pathway changes in multiple immune-related pathways in all of the datasets. In particular, our results indicate that key pathways involved in the control of immune and inflammatory functions were upregulated during DPN, including NF-κB and JAK/STAT pathways. In fact, these pathways are activated in the DRGs of diabetic rats and have been associated with nerve injury in diabetes39. Our findings support targeting these pathways in murine models of diabetes to understand their pathophysiological roles in DPN40. We found that both pro- and anti-inflammatory cytokine pathways were dysregulated across datasets: IL-2, IL-6, and IL-10 as well as chemokines were all altered in DPN. Studies have shown that cytokines and chemokines not only promote existing inflammatory and immune responses, but also induce oxidative and nitrosative stress, further exacerbating cellular injury in experimental models of DPN41. In fact, during inflammation, both pro- and anti-inflammatory pathways are often simultaneously engaged as a disease process such as DPN transitions from active to chronic inflammation. For instance, neuronal repair is initiated by neutrophils42 and driven by macrophages43 under specific environmental conditions that are anti-inflammatory; however, as the disease process progresses, the introduction of pro-inflammatory signals overrides the anti-inflammatory response, resulting in tissue destruction. This concept is established in neurodegenerative disorders such as amyotrophic lateral sclerosis44, and our data strongly suggest a similar process is occurring in DPN.In agreement with our previous studies45 by signalling through receptors such as the Receptor for Advanced Glycation End-products (RAGE) and Toll-Like receptors (TLRs), both of which have been implicated in obesity-driven inflammation and DPN49. This signalling cascade may drive NF-κB pathway that further exacerbates the inflammatory response and increases tissue damage.The importance of the immune system is further highlighted by the fact that many of the pathways we observed dysregulated across all murine models and human samples, including those involved with transcription, cellular development, and lipid metabolism are also involved in the immune response. We show that HMGB1 signalling, which is centrally involved in the regulation of gene transcription, is one of the most highly dysregulated canonical pathways. However, HMGB1 is also secreted by macrophages and damaged cells and mediates systemic inflammation9. Yet, PPAR-γ can also control inflammation in macrophages and dendritic cells51. Besides its established role in inflammation, the LXR/RXR system has emerged as a key regulator of cholesterol, fatty acid and glucose homeostasis, and neuroprotection54. Additionally, LXR/RXR has been increasingly shown to play an important role in diabetic complications57. Interestingly, LXR/RXR is observed as a shared pathway across murine models58. To a further extent, our results show an initial downregulation of the LXR/RXR pathway in the db/db mouse model after 8 weeks of diabetes and an upregulation at late stages of the disease. In contrast, LXR/RXR expression was upregulated in the ob/ob mouse model throughout the duration of diabetes and after 34 weeks of diabetes in STZ-induced T1DM mice, while no data were reported at earlier time points for T1DM mice. Collectively, our findings point toward a potential involvement of the LXR/RXR pathway in DPN. Taken together, our data further support a pivotal role of the immune system in DPN, though it is unclear from the current data to what extent this involvement is a cause or consequence of neuronal damage. We are currently addressing this question in both experimental27 and clinical settings .Moreover, disrupted lipid metabolism is associated with inflammation. PPAR-γ, a major regulator of lipid and glucose metabolism, is altered in murine models as well as in human and has been previously implicated as a common factor in both DPN and diabetic nephropathy18. There are several potential explanations for these discrepancies. The first is that the kinetics of murine and human DPN progression are different; we observed transcriptomic changes in mouse models of DPN up to 24 weeks (T2DM) and 34 weeks (T1DM), but it is unlikely these mice completely represent the advanced stages of DPN encountered in human after decades of disease. Tissue sources may also account for these differences, as the human dataset was obtained from sural nerves while mouse data were collected from sciatic nerves. We are currently exploring discordant transcriptomic dysregulation in the sciatic and sural nerves of mouse models, as a similar effect may be occurring here.On a different note, while numerous pathways were dysregulated across datasets, the direction of transcriptional change was rarely universal. For many pathways, we found that mouse models of DPN and human tissue transcription were differentially expressed in opposite directions; whereas pathways in mice were upregulated, they were frequently downregulated in human patients. Similar cross-species discrepancy in gene expression direction was also observed in diabetic nephropathy between human and murine modelsdb/db microarray data at 8, 16, and 24 weeks12 used in the current study. This study identified various DPN progression-associated genes and pathways, which were overlapping with those identified in the present study, such as TLR signalling, dendritic cell maturation, LXR/RXR activation, and various cytokine pathways. Furthermore, O’Brien et al.’s original publication on the ob/ob mouse model data at 5 and 13 weeks6 included a similar comparative analysis result. In this study, inflammatory mechanisms were found to have a critical role in early development and progression of DPN, and genes related to inflammation, immune response, and chemotaxis were highly enriched at 5 weeks rather than 13 weeks. Both studies identified MMP12 as the most significantly up-regulated gene across different time points, which was also found to be shared between human and murine DPN in the current study, suggesting its potentially critical role in the progression of DPN in both the experimental and clinical settings. Finally, in order to compare changes between species, an orthologue conversion was also used to convert murine gene identifiers to their human equivalent genes; as such it is possible that some murine genes were not captured in the current analyses.Alternatively, the nature of the controls in each species may have influenced the results. Whereas changes observed in the murine models are a result of a diabetic versus non-diabetic comparison, human transcriptomic data are a result of comparisons between progressive and non-progressive DPN. We recently conducted a transcriptional network analysis of DPN progression using the same 17. Together these data suggest that key molecular pathways are commonly involved in DPN but that they may be differentially regulated in T1DM and T2DM. This concept is further supported by our reports in man, where a systemic review of clinical trials strongly suggest different pathogenic mechanisms underlying DPN in T1DM versus T2DM59.We also found that many pathways that were downregulated or a mix of up/down regulation in T2DM-driven DPN were strongly upregulated in the dataset from the T1DM STZ mouse model. To validate these data, we re-examined the outlier parameters in the dataset as well as the background levels of each microarray; in both cases the exclusion criteria remained unmet (data not shown). The STZ dataset was held to the same criteria as all other murine models which allows for a unified comparison across models and species, yet we still found that most of the detected pathways were highly upregulated in T1DM. These data suggest that while similar molecular pathways are involved in DPN progression, the utilization of these pathways may be very different in T1DM and T2DM. This is supported by our previous study examining transcriptional changes in diabetic neuropathy and nephropathy in both T1DM and T2DM mouse models; we found that while there was a high transcriptional concordance in diabetic nephropathy, DPN-associated pathways were often highly discordantIn summary, our transcriptional network-based approach, integrating multiple bioinformatics analyses, identified DPN-associated pathways that are highly conserved across multiple murine models and human. Many of the pathways identified highlight the importance of the immune system through cytokine and chemokine signalling as well as the observed dysregulated pathways associated with transcription, cellular development, and lipid metabolism are all involved in the immune response. The observed conserved pathways are likely the key responses in DPN and provide new therapeutic targets for the potential treatment of DPN, a disorder that remains without a drug intervention to date.Supplementary Materials


Genetic and biochemical screening approaches often fail to identify functionally relevant pathway networks because many signaling proteins contribute to multiple gene ontology pathways. We developed a DRUGPATH-approach to predict pathway-interactomes from high-content drug screen data. DRUGPATH is based upon combining z-scores of effective inhibitors with their corresponding and validated targets. We test DRUGPATH by comparing homeostatic pathways in human embryonic stem cells (hESCs), human induced pluripotent stem cells (hiPSCs) and human amniotic fluid stem cells (hAFSCs). We show that hAFSCs utilize distinct interactomes compared to hESCs/hiPSCs and that pathways orchestrating cell cycle and apoptosis are strongly interconnected, while pathways regulating survival and size are not. Interestingly, hESCs/hiPSCs regulate their size by growing exact additional sizes during each cell cycle. Chemical and genetic perturbation studies show that this “adder-model” is dependent on the DNA-damage pathway. In the future, the DRUGPATH-approach may help to predict novel pathway interactomes from high-content drug screens. Therefore, knowledge about pathways they utilize to regulate the most essential homeostatic processes like survival, apoptosis, cell cycle and size are of utmost relevance.Human ESCs have no expansion limit and represent a source for differentiated cells stemming from all three embryonic germ layers. Owing to these properties, hESCs hold a great promise for regenerative medicine. Whether or not regenerative medicine will become applicable in the future depends largely on our ability to derive and exploit ethically unproblematic cells with the closest possible characteristics to hESCs. These cells have become the golden standard for comparisons with 3. Drug-screens are suitable for the analysis of homeostatic pathways because small-molecule inhibitors may act potently and instantaneously on their specific targets. However, intracellular networks are largely redundant, - i.e. many signaling proteins are shared between pathways - and thus, inhibition of one component will affect multiple pathways. Additionally, inhibitors have different specificities for on-targets and may also provoke off-target responses. Therefore, when screening, it is recommendable to use multiple inhibitors targeting the same on-target pathways. Comparable functional responses arising from multiple inhibitors targeting the same specific targets point to genuine on-target effects of the respective inhibitors. In contrast, distinct functional responses arising from inhibitors targeting the same specific targets may indicate that involvement of yet unknown pathways. In this study, we employ the power of bioinformatics to predict involvement of more distant pathways from the screening data. Generally spoken – very similar responses from inhibitors that are supposed to target the same pathway will minimize the number of outlier pathways. In contrast, different or even contrary responses of inhibitors targeting the same pathway will increase the amount of possibly involved pathways.So far, various approaches have been undertaken to identify molecular pathways that govern these processes. For instance, global transcription profiling qualifies for identification of genes which are up or downregulated between certain cellular states, but it is rather unsuitable for identification of steady-state pathway networks. In addition, many changes in transcriptional gene expression are rather consequences of unknown upstream signaling networks. Large scale RNAi-screens are more persuasive, however, they generate a large number of false-positives, yielding only few specific pathways5 and differentiation8. Regulation of cell cycle, size and the balance between survival and apoptosis are highly evolutionary-conserved processes that need to be constantly maintained. So far, homeostatic mechanisms regulating cell cycle, size and viability have remained largely enigmatic in hESCs, hiPSCs and hAFSCs12.Comparisons between hESCs and hiPCSs have been undertaken to elucidate the mechanisms of pluripotency by functional screening, with major focus on cellular viabilityIn this study, we combine distinct bioinformatic methods to identify pathway interactomes that regulate cell cycle, size, survival and apoptosis of hESCs, hiPSCs and hAFSCs. Using the DRUGPATH-approach, we are able to predict pathway interactomes from hits which we obtained in a high-content inhibitor screen. We confirm the outcomes of our screen by validating previously published pathways  using chemical and genetic manipulation. Finally, we also identify a novel regulator of size in hESCs/hiPSCs - the ATM-signaling pathway.5 and was aiming at reducing off-target effects. In most cases, we used two to four different inhibitors targeting the same pathway.We hypothesized that homeostatic processes may be differentially regulated in hESCs, hiPSCs and hAFSCs. To identify pathway networks, which regulate homeostatic processes, we measured changes in survival, apoptosis, cell cycle or size upon treatment with 81 selective small molecule inhibitors using flow cytometry. To this end, all cells have been stained intracellularly with propidium-iodide (PI) and information about ten different homeostatic parameters has been obtained simultaneously . To avoid this unintentional effect, we sought to reduce duration of inhibitor treatments. This seems reasonable because previous work has shown absence of differentiation markers before day 3 of hESC-differentiationers Fig. ,B. Imporers Fig. .15 and developed a method allowing colony-type cultivation of hiPSCs and hESCs in a 96-well format .We have screened 9 different cell lines: 3 hiPSC lines , 3 hAFSC lines  and 3 hESC lines  in technical triplicates. To avoid identification of unspecific pathways, we deployed distinct biological replicates and - where available - we used cell lines of distinct genetic backgrounds, tissue origins, reprogramming methods and sex Table . To prevWe developed a bioinformatic workflow that we termed DRUGPATH - consisting of 10 steps, which ultimately lead to predictions of pathway-interactomes from drug-screening data Fig. . We coulNext, we statistically discriminated between effective Table  and non-st and 2nd branching). For example, inhibitors affecting “survival”  of all significant hits  with selective HDAC1/3-inhibitor Entinostat strongly influenced the expression of cyclinD1 in WA01, but not in A1 cells  from the screen data and correlated them to the corresponding “sizes at birth” (G1). Interestingly, the size checkpoints were differentially regulated in hESCs/hiPSCs and hAFSCs: hESCs/hiPSCs follow the ‘adder’-model by growing equal volumes and equal surfaces before division irrespective of the ‘birth size’. In contrast, hAFSCs showed a negative slope and thus obey to the ‘sizer’-model  are correlated to corresponding initial sizes in G1 (“size at birth”)del Fig. . Furtherdel Fig.  – which 23. Depending on the nutrients and on the time they need to fulfill this synthesis, cells may change the relative ratio of surface area (SA) to volume (V) during cell cycle – thereby becoming smaller or larger depending on culture conditions. Notably, this model has only been shown for bacteria. Our prediction for the stem cells was that hESCs/hiPSCs with blocked DNA-damage signaling would reduce their size and accordingly increase their SA/V-ratio. We treated distinct cell groups with Tenovin-1 and Torin-2, which specifically inhibit two components from different DNA-damage-pathways, Sirtuin1/2 and ATM, respectively. The relative contribution of Sirtuins to different DNA-damage pathways is not yet clear24. We calculated the SA/V-values using the mathematical assumption that cells resemble a spherical ball . Indeed, treatment with both inhibitors increased the SA/V-ratios during the G1-phase specifically in hESCs/hiPSCs . Ultimately, we have identified a novel role for serine-threonine kinase ATM in size-maintenance of hESCs and hiPSCs.27. We sought to identify essential pathways by screening for functional differences between the stem cell groups. This is highlighted by the total number of pathways identified in this study compared with previous screening data4 on which our methodology has been subsequently applied , we could deduce information about all validated targets and combine them with the magnitude of response measured in our screen. Using this information, we were able to bioinformatically calculate pathways that may have been affected by specific inhibitor hits.on functional and molecular level, hESCs, hiPSCs and hAFSCs share some similarities  and some differences . In this study we assumed that these differences may arise from distinct programs (i.e. large protein networks), which distinct stem cells utilize to maintain their homeostatic processes like regulation of survival, cell cycle and size. Previous work has suggested that half-lives of proteins regulating homeostasis can be several magnitudes longer than a mammalian cell cycle and that – in general – mRNA abundance is not well correlated with protein abundancehttp://software.broadinstitute.org/gsea/msigdb): C2: KEGG and Reactome as well as C5: BP , CC (cellular component) and MF (molecular function). Finally, visualization of nodes and connections has been performed using the Cytoscape app (step 10).Usually, outputs generated by drug-screens are lists of candidate inhibitors with statistically significant effectivity to a specific biological response in certain cell groups. These inhibitors need to be further validated in order to confirm their action. However, inhibitors have many targets and each target has many roles in different signaling pathways. Thus, to predict pathways that have directly been influenced by effective inhibitors, we developed the DRUGPATH-approach. The DRUGPATH-approach uses two variables from a drug-screen – the effective inhibitor (hit) gene name and the strength of its effectivity in the screen. To make this effectivity measure a useful variable, we first calculated z-scores  for each condition and process applied in the screen. The advantage of z-scores is that they do not reduce relative amplitude of specific responses above noise and that they do not generate 0 values, which are useless in GSEA. In detail, we followed these principles for DRUGPATH-analysis Fig.  and noisFacs data have been pre-analyzed using Facs Diva Software and exported to FlowJo for further analysis. Each sample has been checked individually and gatings adjusted if necessary /σ, where x represents a normalized raw value of each cellular process , µ is the mean of all values in a certain cellular process and σ is the standard deviation of all values of a certain cellular process.The entire analysis was done within R statistical scripting language environment (v.3.2). For comparisons between hPSC-groups, z-scores values, log2-values and raw values were considered for calculation using R’s ‘pairwise.t.test’ function. Benjamini & Hochberg adjustment method was applied to the p-values for multiple testing corrections. In addition, all comparisons have been calculated in GraphPad Prism 6. Normal data distribution has been tested using Kolmogorov-Smirnov test and calculated using One-way Anova and Dunnett’s multiple comparison post-test. All comparisons yielded >95% identical hits (data not shown). P < 0.01  was accepted as statistically significant.https://github.com/karinschlangen/stemCells_pathwaysIn order to visualize the correlations between the z-score values of the different samples, heatmaps of the matrix of Pearson correlations between the samples were plotted using the ‘heatmap.2’ function from the gplots package. Furthermore, heatmaps of inhibitor- z-scores with significant differences in means between the different groups  were plotted. Statistics were calculated using the ‘pairwise.t.test’ as described above. Hierarchical cluster analysis was performed using R’s ‘hclust’ function using the Euclidean distance matrices and a complete linkage clustering of Spearman correlation values of the inhibitor z-scores between the samples. Principal component analysis (PCA) was performed with the transposed z-scores using the R function ‘prcomp’, and the first two principal components were plotted. A full script can be obtained from here: http://software.broadinstitute.org/gsea/msigdb): C2: KEGG and Reactome as well as C5: BP , CC (cellular component) and MF (molecular function). Analysis was performed with 1000 permutations on pre-ranked gene lists using the fold changes of the respective comparisons.For superior-network analysis, we separately considered all significant inhibitors of survival, apoptosis, G1-phase, S-Phase, G2/M-phase, size of G1-phase cells, size of S-phase cells, size of G2/M-phase cells, size of apoptotic cells and size of all cells. We hypothesized that inhibitor-groups showing similar effects on hPSC-groups belong to common pathway networks utilizing common core proteins. Similar effectors have been consolidated into subclusters, yielding a total of 29 subclusters for all interrogated biological processes. Lists with significant inhibitors have been transformed into gene lists containing all published inhibitor targets effective at <5 µM concentrations Table . To idenSignificantly affected pathways/GO terms (Table Supplementary Methods, Figures S1–18, LegendsTable S1Table S2Table S3Table S4Table S5Table S6Table S7