Genomic heterogeneity in human cancers complicates gene-centric personalized medicine. Malignant tumors often share a core group of pathways that are perturbed by diverse genetic mutations. Therefore, one possible solution to overcome the heterogeneity challenge is a shift from gene-centric to pathway-centric therapies. Pathway-centric perspectives, which underscore the need to understand key pathways and their critical properties, could address the complexity of cancer heterogeneity better than gene-centric approaches to aid cancer drug discovery and therapy.p values and Benjamini–Hochberg correction for multiple hypotheses testing.We used large-scale pharmacogenomic profiling data provided by the Cancer Genome Project of the Wellcome Trust Sanger Institute and the Cancer Cell Line Encyclopedia. In a systematic in silico investigation of ERK signalling pathway components and topological structures determines their influences on pathway activity and targeted therapies. Mann–Whitney U test was used to identify gene alterations associated with drug sensitivity with The analysis demonstrated that genetic alterations were crucial to activation of effector pathway and subsequent tumorigenesis, however drug sensitivity suffered from both drug effector and non-effector pathways, which were determined by not only underlying genomic alterations, but also interplay and topological relationship of components in pathway, suggesting that the combinatorial targets of key nodes in perturbed pathways may yield better treatment outcome. Furthermore, we proposed a model to provide a more comprehensive insight and understanding of pathway-centric cancer therapies.Our study provides a holistic view of factors influencing drug sensitivity and sheds light on pathway-centric cancer therapies.The online version of this article (doi:10.1186/s40169-015-0066-1) contains supplementary material, which is available to authorized users. The principle of personalized cancer therapy is to develop therapeutic strategies targeting specific genomic alterations and/or perturbed pathways associated with specific cancers. Much of the ongoing effort has been directed at identifying individual biomarkers of effective therapies and developing anticancer drugs targeting specific genomic alterations –4. HowevMalignant tumors often share a core group of about a dozen perturbed pathways  that regSOX2 and TP63; loss-of-function of NOTCH1, NOTCH2, and ASCL4; and focal deletions in FOXP1  or redundant pathways other than BRAF or MEK1/2 to transfer ectopic EGFR signals . Our analysis suggests that there might exist the effective targeting nodes as potential pathway-centric therapies, which should locate in the downstream of a pathway and be a unique path for transferring signalling.Cells harboring lls Fig. d. EGFR iBRAF, KRAS, and NRAS mutations were still sensitive to MEK1/2 inhibitors (Fig. We used the independent CCLE dataset to confirm our findings. In CCLE data, the IC50 could not be estimated in many cases, as drug concentration necessary to inhibit 50% of growth was not reached. Therefore, different from IC50 in CGP data, drug response in CCLE data was presented by the value of activity area and greater value means higher sensitivity see “”. In thiors Fig. a, b.Fig.KRAS, BRAF, and NRAS mutations [Further, CCLE analysis from the view of topological structures also showed consistency with our previous CGP findings Fig. c–e. Our utations .Based on our analyses, we generated a conceptual model to delineate pathway-driven personalized medicine. In this model Fig. , all patKRAS, BRAF, or NRAS mutations. In two cases with no drug effect, the drug is not targeting the perturbed pathway or altered downstream genes gained ectopic activation independent of upstream stimulation, so treatment is ineffective. In two cases with partial drug effect, the first case simulates a tumor with multiple perturbed pathways. Single drugs targeting one pathway are often therapeutically ineffective because other perturbed pathways still provide cellular growth or survival stimulation. In the second case with partial drug effect, the drug could not completely inhibit the activated pathway because its targets were not unique molecules for activation of the downstream pathway. In this case, the drug has little effect on cell growth.In the case with good drug effect, drug effector pathways are abnormally activated, which could be caused by two different mutant oncogenes in this pathway. Drug non-effector pathways have normal signalling. In this case, drugs targeting effective targeting nodes of this perturbed pathway usually work well. The effective targeting nodes should be downstream and unique molecules in this pathway. By inhibiting these molecules, drugs are more likely to inhibit the activity of the whole pathway. For instance, MEK inhibitors completely inhibit ERK signalling by targeting MEK1/2 kinases, and are effective in treatment of tumors harboring In this study, we systematically investigate ERK signalling pathway components and topological structures that determine their influences on pathway activity and targeted therapies. A tumor is a dynamic entity that evolves by acquiring a series of mutations with advantageous phenotypes, therefore a tumor in general has multiple perturbed pathways. Our analyses demonstrate that genomic alterations may affect multiple pathways, which could be classified as drug effector and non-effector pathways by the way they target the two cancer hallmarks ‘sustaining proliferative signaling’ and ‘evading growth suppressors’ . Both thMany pathway-centric studies were previously reported –27. For Our analysis showed that cells with EGFR mutation were not statistically more sensitive to EGFR inhibitors than cells without EGFR mutation for any inhibitors. In clinic EGFR inhibitors gefitinib and erlotinib are widely used in non-small-cell lung cancer patients with EGFR mutations for their dramatic efficacy –30. HoweDespite the encouraging results, our approach suffers from the following limitations. First, some confounding factors, such as the cancer-type from which a cell line has been originated, or its genomic instability, may contribute to drug sensitivity. Stratification analysis or multivariate analysis could be preferred to determine the association between gene alterations and drug sensitivity. However, when focusing on the individual classes of mutations in the context of ERK signalling pathway, stratification analysis or multivariate analysis could strongly reduce the population size for some test. Therefore, in this study univariate statistical test was used to determine gene alterations contributing to the increased level of resistance/sensitivity to a given drug. This analysis could over-estimate the significance of their association. Second, a recent study revealed significant discordance between CGP and CCLE datasets. These two studies used different experimental protocols. Differences include the pharmacological assay used, the range of drug concentrations tested, and choice of an estimator for summarizing the drug sensitivity . With thWe applied statistical methods to analyze two recently published pharmacogenomic datasets to investigate pathway components and topological structures and their influences on targeted cancer therapies. The results show that activity of drug effector and non-effector pathways as determined by genomic alterations influence drug sensitivity, and key targeting nodes in specific pathways may yield better therapeutic predictions by pathway-centric rather than gene-centric approaches. We propose a model to provide a more comprehensive understanding of pathway-centric cancer therapies, which requires future in vitro and in vivo studies to validate the genes predicted to be affecting drug sensitivities. Our study demonstrates how pharmacogenomic profiling could be used to shed light on pathway-centric personalized cancer therapies.


Kinetoplastea is a diverse protist lineage composed of several of the most successful parasites on Earth, organisms whose metabolisms have coevolved with those of the organisms they infect. Parasitic kinetoplastids have emerged from free-living, non-pathogenic ancestors on multiple occasions during the evolutionary history of the group. Interestingly, in both parasitic and free-living kinetoplastids, the heme pathway—a core metabolic pathway in a wide range of organisms—is incomplete or entirely absent. Indeed, Kinetoplastea investigated thus far seem to bypass the need for heme biosynthesis by acquiring heme or intermediate metabolites directly from their environment.Perkinsela spp., kinetoplastids that live as obligate endosymbionts inside amoebozoans belonging to the genus Paramoeba/Neoparamoeba. We also use phylogenetic analysis to infer the evolution of the heme pathway in Kinetoplastea.Here we report the existence of a near-complete heme biosynthetic pathway in Perkinsela spp. is a deep-branching kinetoplastid lineage, and that lateral gene transfer has played a role in the evolution of heme biosynthesis in Perkinsela spp. and other Kinetoplastea. We also discuss the significance of the presence of seven of eight heme pathway genes in the Perkinsela genome as it relates to its endosymbiotic relationship with Paramoeba.We show that The online version of this article (doi:10.1186/s12862-016-0664-6) contains supplementary material, which is available to authorized users. Phytomonas [Angomonas [Leishmania [Trypanosoma [Bodo saltans [Neobodo designis [Ichthyobodo necator, a fish ectoparasite, and Perkinsela sp. [Neoparamoeba/Paramoeba spp. [Diplonema papillatum) and Euglenida such as the plastid-bearing organisms Eutreptiella gymnastica and Euglena gracilis.Kinetoplastea is a diverse group of unicellular flagellated organisms, most of which are parasites. The best known group of kinetoplastid parasites is the Trypanosomatida, which parasitize plants , insectngomonas ) and humishmania  and Tryppanosoma ). Howeve saltans  and Neobdesignis . The boddesignis –10 and sdesignis . Howeverdesignis , 10, 12.designis –9, and isela sp. , which ieba spp. –18. The Strigomonas culicis and Angomonas deanei, from bacterial endosymbionts [Phytomonas serpens seems not to require heme at all [Trypanosoma and only the last three steps of the pathway are present in other trypanosomatids such as Leishmaniinae  [Angomonas spp. and Strigomonas spp.). No complete heme pathway has been described for a member of the Kinetoplastea [Parasitic Kinetoplastea have complex life cycles and have undergone extensive reductive evolution as a consequence of their parasite-host interactions. One commonly observed change is the reduction or complete loss of biochemical pathways that can be augmented or provided by their hosts . This inymbionts . Metabolymbionts , or hemeymbionts , 27. Fure at all . The hemia spp.)  and Strioplastea .The heme pathway is an important part of cellular metabolism. It produces the cofactor heme, which is required for key biochemical processes such as oxidative phosphorylation. In most eukaryotes, heme biosynthesis involves eight steps Fig. , the firIn the next step of the heme pathway, coproporphyrinogen III is modified to protoporphyrinogen IX by coproporphyrinogen III oxidase (CPOX/HemF) under aerobic conditions, and under anaerobic conditions, by oxygen-independent coproporphyrinogen III oxidase (CPOX/HemN) . SubsequPerkinsela, which are endosymbionts of the amoebozoans Paramoeba pemaquidensis and P. invadens. Using molecular phylogenetics, we show that Perkinsela belongs to the earliest branching kinetoplastid lineage currently known, and demonstrate that these highly reduced endosymbionts nevertheless possess a near-complete heme biosynthesis pathway, the first of its kind described for a member of the Kinetoplastea. We hypothesize that at least a subset of these enzymes represents elements of the ancestral heme pathway in the group. Finally, we discuss the importance of this pathway in early-branching kinetoplastid flagellates for understanding the adaptations that have occurred during the evolution of bodonids and parasitic trypanosomatids.In this study, we analyzed the heme pathway of two species of Perkinsela endosymbiont living within the amoebozoan Paramoeba pemaquidensis CCAP1560/4. The nuclear genome (and associated transcriptome) of the host P. pemaquidensis was also queried, as was transcriptomic data from another species, P. invadens. GenBank accession numbers for the Perkinsela spp. and Paramoeba spp. data used in this study are LFNC00000000 and KU609011-KU609043. BLASTp/tBLASTn searches were carried out using a curated set of heme pathway enzymes from diverse eukaryotes as queries with an E-value cut-off 1e-05. For identification of UROS enzymes, local HMMER [hmmsearch) were initially performed against the total P. invadens transcriptome database (6-frame translation into protein) using default settings. Profile HMMs were constructed via hmmbuild with Stockholm alignments (‘Seed’ and ‘NCBI’) for HEM4 (PF02602) retrieved from the Pfam website (http://pfam.xfam.org). Hits with an E-value ≤ 1e-05 were then used as queries to screen the P. pemaquidensis total genomic and transcriptomic assemblies via local tBLASTn. Homologs were also identified using Ghostkoala . Sequences used in phylogenetic analyses were obtained by BLAST from the NCBI nr database, the MMETSP database of transcriptomes [B. saltans genome (Welcome Trust Sanger Institute). To further verify their predicted roles in heme biosynthesis, all sequences analyzed in this study were annotated using InterProScan [We searched for genes encoding heme biosynthetic enzymes in the nuclear genome of the al HMMER  searchesriptomes , from TrrProScan  and the rProScan .http://www.cbs.dtu.dk/services/SignalP-3.0/) [http://www.cbs.dtu.dk/services/TargetP/) [http://aias.biol.uoa.gr/PredSL/input.html) [https://urgi.versailles.inra.fr/predotar/predotar.html) [http://www.cbs.dtu.dk/services/TMHMM/) [Sequences of proteins putatively involved in heme biosynthesis were subjected to a localization prediction pipeline using the following tools: SignalP 3.0 (lP-3.0/) , TargetPargetP/) , PredSL ut.html)  and Predar.html)  with sta/TMHMM/)  was used/TMHMM/) , 52. PlaPerkinsela spp. in particular . Phylogenetic trees were first constructed individually for each protein. Homologs were aligned using MUSCLE [http://tree.bio.ed.ac.uk/software/figtree/). Topological tree tests were performed using RAxML version 8.0.19 [Eleven proteins were selected for their potential to resolve the evolution of Kinetoplastea in general and the taxonomic position of g MUSCLE  and blocg MUSCLE . The twog MUSCLE  model win 8.0.19 , the ShiWe also built phylogenetic trees for enzymes involved in the heme pathway in Kinetoplastea. Sequences were retrieved using homology searches by BLAST against sequences obtained from different sources (see above). All sequences with an E-value less than 1e-5 were selected. We then aligned these sequences using MAFFT with the fast alignment settings . Block sPerkinsela spp. group, with representatives from trypanosomatids and bodonids, and rooted with the diplonemid Diplonema papillatum, the euglenid Eutreptiella gymnastica and the heterolobosean Naegleria gruberi. The eleven proteins were carefully selected based on their availability in public databases in the lineages of interest  so as to minimize missing data in our supermatrix. Our results confirm that the diversity of Perkinsela spp. for which genomic and/or transcriptomic sequence data are currently available represent a monophyletic assemblage, as previously described [N. gruberi to see if the Prokinetoplastina was still positioned as the deepest branch of the Kinetoplastea , with the exception of one protein , we found a full set of heme biosynthesis enzymes encoded in the nuclear genome of the Perkinsela spp., we inferred the presence/absence of heme biosynthesis enzymes in all the organisms present in the phylogenetic tree shown in Fig. Perkinsela spp. reside  enzyme. However, we also found a potential uroporphyrinogen-III synthase enzyme in Leishmaniinae, which has not previously been discussed. In addition, we identified an almost complete heme pathway in the euglenid E. gymnastica, and, as expected for a plastid-bearing organism, the alga-associated GTR and GSA-AT enzymes [Diplonema papillatum, noting that at present only a small amount of sequence data is publicly available.Beyond  enzymes . We founPerkinsela spp. appear to lack only one enzyme of the heme biosynthetic pathway (UROS), making it the most complete set identified for a kinetoplastid thus far. Other kinetoplastids have only a partial pathway or have lost the capacity to synthesize heme entirely; presumably they obtain heme from their host or do not require it. Perhaps due to its incomplete nature (when present), the subcellular localization of the heme pathway in other Kinetoplastea is different from the classical heme pathway in other eukaryotes [Perkinsela sp. and compared these data to what is known from other kinetoplastids. We also predicted the localization of the heme pathway in the host amoebae, and compared them to other amoebozoans  and posterior probabilities (PP) of 70 % and 0.99, respectively), but weak for ALAS. In both cases, homologues can be found in virtually all major eukaryotic groups and they seem to form a monophyletic clade . This suggests that UROD and ALAS were likely present in the eukaryotic common ancestor and have been vertically inherited in Perkinsela spp. In addition, the eukaryotic ALAS homologues branch within alpha-proteobacteria, strongly suggesting a mitochondrial origin.Given the patchy distribution of heme pathway enzymes in Kinetoplastea, we performed phylogenetic analyses for the complete set of ROD Fig.  and ALASROD Fig. , the PerPerkinsela spp. proteins generally branched amongst eukaryotic sequences, with bacterial sequences either being more distantly related or patchily distributed  events in the history of this enzyme in Kinetoplastea . In addemF Fig. , while tPhytomonas [Kořenỳ et al. proposed that heme enzymes in trypanosomatids have been acquired by LGT from various sources . Indeed,ytomonas .Parabodo caudatus, the FeCH homolog appears to be derived by LGT from Gammaproteobacteria  – the one included in the phylogenetic tree , branch somewhat close to one another  found in this part of the UROS tree are separated by different bacteria, it seems reasonable to assume that the putative UROS gene of Leishmaniinae was acquired by LGT from bacteria.While we were unable to detect any UROS related enzyme genes in the genome of the ree Fig.  – shows her Fig. , with thPerkinsela spp. homologs do not branch with those of Euglenozoa or Eukaryota , one thview see –78. A roPerkinsela spp. were most likely inherited vertically, since they seem to share recent common ancestry with euglenozoan enzymes. On the other hand, the genes for ALAD, PBGD, CPOX/HemF, and PPOX/HemY could represent more recent acquisitions by LGT, perhaps from a eukaryotic donor. However, homologs of CPOX/HemF, PPOX/HemY and ALAD are widely distributed amongst eukaryotes and they seem to form a monophyletic clade. In addition, the position of the Prokinetoplastina at the root of eukaryotes seems to not argue in favor of recent acquisitions by LGT, particularly in light of recent articles that place excavates at the root of the eukaryotic tree [On one hand, the ALAS and UROD enzymes of tic tree –81.Perkinsela spp. gene was either acquired by LGT from Bacteroidetes or present in a common ancestor shared with Euglenozoa. Under the scenario of it being present ancestrally, the presence of Bacteroidetes homologs in this part of the tree could mean LGT from Euglenozoa to Bacteroidetes  is consistent with this possibility. Alternatively, the Leishmaniinae and/or other eukaryotes could have acquired the putative UROS gene by LGT from bacteria independently.The only enzyme of the heme biosynthetic pathway that appears to be absent from the Paramoeba pemaquidensis nuclear genome, which could indicate two different UROS functions in the host amoeba. However, the level of sequence conservation of one of the two putative host UROS enzymes  is so low that it was not possible to include it in our phylogenetic analysis. We are thus presently only confident in the presence of one UROS enzyme in the Paramoeba species we investigated (KU609032 in P. pemaquidensis). In any case, the apparent absence of a gene encoding UROS in the Perkinsela sp. genome, and the presence of at least one UROS in Paramoeba spp., raises the possibility of metabolic interdependency between the two cells. It is conceivable that a host-encoded UROS protein and/or metabolic intermediates make their way to the kinetoplastid endosymbiont in order to compensate for the gap in the heme biosynthetic pathway of the endosymbiont. Further research is needed to confirm or refute this hypothesis (see below).Interestingly, we found two potential UROS candidates in the Phytomonas and also in Bodonida [Phytomonas groups with the Leishmaniinae genus Leptomonas, and could be the product of a separate LGT event  cannot be rejected outright  exchange between the two? Interestingly, heme transporters have recently been characterized in Leishmania [Trypanosoma brucei might be receptor-mediated [Leishmania and Trypanosoma heme transporters in the nuclear genome of Perkinsela sp. Two scenarios are possible: either the evolution of such heme transporters in an ancestor of kinetoplastids took place after the divergence of Prokinetoplastina, or the transporters were lost in Prokinetoplastina. Since the Prokinetoplastina sequences currently available belong to intracellular endosymbionts, it is possible that the related transporters have been lost as a result of reductive evolution.Given that seven of the eight ‘core’ heme pathway enzymes exist in ishmania , 84 and mediated , 85. We Leishmania and Trypanosoma [Perkinsela sp. all the more intriguing. Given its obligate intracellular lifestyle, it would not be surprising if it were capable of taking heme, or at least intermediates of the heme biosynthetic pathway, from its host. A common theme in cellular evolution is the reduction of pathways in organisms that live (endo) symbiotically [Perkinsela spp. Although only one heme pathway enzyme appears to be missing in Perkinsela spp., it is a key component and suggestive of host-endosymbiont metabolic dependency.While the heme biosynthetic pathway provides heme as a cofactor for metabolic processes such as oxidative phosphorylation, growing evidence indicates that some organisms can thrive without it , 86. Redpanosoma , which motically , 88, parPerkinsela sp. endosymbiont, the reaction producing protoporphyrinogen IX is most likely catalized by CPOX/HemF, while the amoeba host possesses both CPOX/HemF, which acts in aerobic environments, and a CPOX/HemN homolog predicted to function anaerobically. Thus under anaerobic conditions, only the host might be capable of producing the required heme intermediates needed by both organisms. Indeed, even in the absence of specific transporters for heme or heme pathway intermediates, it is possible that non-specific mechanisms such as endocytosis might play a role in host-endosymbiont heme exchange. A better understanding of the metabolic and cell biological communication between host and endosymbiont will hopefully shed light on the nature of this unusual endosymbiotic relationship.In addition, it is interesting to note that in the Perkinsela spp.) was ancestrally present. LGT also appears to have contributed heme biosynthetic genes after the various Kinetoplastea lineages diverged from one another, perhaps related to their adaptation to a parasitic lifestyle. The presence of an almost complete heme pathway in Perkinsela spp. provides an opportunity for future studies aimed at shedding light on the nature of the metabolic connections between Perkinsela and its Paramoeba host.Here we have explored the basal evolutionary position of Prokinetoplastina within Kinetoplastea, and provided the first evidence for the existence of a near-complete heme biosynthesis pathway in a member of the Kinetoplastea. Phylogenetic analyses suggest that a major portion of the heme pathway present in Prokinetoplastina  are available on request and from the CCAP. Phylogenetic data are available in Dryad: https://datadryad.org/resource/doi:10.5061/dryad.664cp.2.All data are available through NCBI. Materials (


To assess relationships between structural and functional biomarkers, including new topographic measures of visual field sensitivity, in patients with autosomal dominant retinitis pigmentosa.2) from mixed-effects analysis with automatic model selection.Spectral domain optical coherence tomography line scans and hill of vision (HOV) sensitivity surfaces from full-field standard automated perimetry were semi-automatically aligned for 60 eyes of 35 patients. Structural biomarkers were extracted from outer retina b-scans along horizontal and vertical midlines. Functional biomarkers were extracted from local sensitivity profiles along the b-scans and from the full visual field. These included topographic measures of functional transition such as the contour of most rapid sensitivity decline around the HOV, herein called HOV slope for convenience. Biomarker relationships were assessed pairwise by coefficients of determination (R2>0.8 in most cases). The best-fit relationship models and correlation patterns for horizontally oriented biomarkers were different than vertically oriented ones. The structural biomarker with the largest number of significant functional correlates was the ellipsoid zone (EZ) width, followed by the total photoreceptor layer thickness. The strongest correlation observed was between EZ width and HOV slope distance . The mean sensitivity defect at the EZ edge was 7.6 dB. Among all functional biomarkers, the HOV slope mean value, HOV slope mean distance, and maximum sensitivity along the b-scan had the largest number of significant structural correlates.Structure-function relationships were accurately modeled (conditional RTopographic slope metrics show promise as functional biomarkers relevant to the transition zone. EZ width is strongly associated with the location of most rapid HOV decline. Retinitis pigmentosa (RP) is a heterogeneous group of genetic disorders that primarily affects photoreceptors and the retinal pigment epithelium, leading to progressive outer retinal thinning and loss of visual function –5. UnderOur goal was to better characterize the functional correlates of anatomic biomarkers in the retinas of autosomal dominant RP patients. We analyzed a diverse set of biomarkers obtained from full-field standard automated perimetry (SAP) and OCT imaging of clinical trial RP patients. Visual fields were interpolated to facilitate alignment with anatomic imagery and topographic analysis of the visual sensitivity surface. Biomarker relationships were represented by one of three possible candidate models and selected by a statistical quality metric. Biomarker extraction and quantitative modeling were implemented with automated methods wherever possible to maintain objectivity and reproducibility.The subjects were participants in the ongoing Trial of Oral Valproic Acid (VPA) for Retinitis Pigmentosa (NCT01233609). The VPA trial is a phase II multicenter interventional study of the safety and efficacy of valproic acid in a cohort of clinically and genetically confirmed autosomal dominant RP patients. The relevant inclusion criteria for the trial were 20/200 or better visual acuity and 18 years minimum age; exclusion criteria were other retinal diseases present, an intact visual field of less than 5°, and unreliable perimetry measurements in both eyes as determined by the reading center. Patients provided written informed consent for data collection and analysis in accordance with the tenets of the Declaration of Helsinki and an institutional review board (IRB) protocol at each site. All patient records were anonymized and de-identified at the time of collection and prior to analysis. This analysis was determined to be exempt from review by the Oregon Health & Science University IRB in accordance with the Department of Health & Human Services regulation 45 CFR 46.101(b)(4).To minimize learning effects and preclude any influence from the intervention, our analysis only included SAP and OCT data acquired during baseline visits after initial screening. Of the 90 participants in the trial, 56 had data available for this study. From these 56 participants, 35 (mean age 52.1 ± 11.0) had suitable data supporting all variables studied in our analysis. Reasons for unsuitable data included: OCT image quality was too poor to identify the retinal layers around the fovea; EZ was not continuously apparent throughout the OCT b-scan; evidence of vitreous traction with an epiretinal membrane or cystic spaces; or the SAP reliability factor  was greater than 20%. Data was available from both eyes of 25 subjects and one eye from 10 subjects, yielding 60 eyes total.Spectral-domain OCT imaging with the Heidelberg Spectralis  was performed at all VPA trial participating sites with 30° scans designed by protocol to include the maximum foveal depression. Horizontal (H) and vertical (V) line scans were obtained in high-resolution mode with active eye tracking and automatic real-time image averaging, yielding b-scans with approximate resolutions of 3.9 μm axially and 6 μm laterally.The retinal layers were segmented by a two-step process: automatic segmentation by the HRA/Spectralis (viewing module 6.0.9.0), then manual correction by three trained graders following a segmentation protocol established prior to this study. Graders had no knowledge of the functional test results. As shown in To investigate the effect of the retinal curvature in the b-scan, a flattened OCT data set was generated. Custom software was developed to fit a cubic polynomial to the pRPE boundary and then flatten the OCT images and segmented boundaries by shifting each pixel column vertically by the polynomial model. The flattened data was analyzed in parallel with the original, unflattened scans.2 background luminance, the GATE-i fast thresholding strategy [Full-field automated static visual field testing with an Octopus 900  was performed at each site using 10 cd/mstrategy , and a 2strategy . ResamplAlthough complementary, SAP and OCT data have disparate information, a low degree of mutuality, and few common features needed by data-driven registration techniques . AlignmeAll biomarkers are listed with their abbreviations in Tables Functional biomarkers were extracted from the patient’s visual sensitivity data, and were classified as either global (from the entire HOV) or local . All were computed from both the patient’s HOV and the defect HOV, the difference between an age-adjusted normal HOV and the patient’s HOV. We included standard indices of visual function such as mean sensitivity, mean defect and loss variance . We alsoWe developed new biomarkers to characterize the topographic slope of the HOV surface and quantify the transition from preserved central field to reduced peripheral field. For convenience, we use the term HOV slope to describe the contour around the center of the HOV where the HOV changes most rapidly. More specifically, the HOV slope contour was defined as the location of the extreme values of the radial gradient, the first derivative taken radially outward along the sensitivity surface. The slope extrema  were identified along each of 501 radial spokes emanating from the center point with 0.72° angular separation. For illustration purposes, eight of the 501 spokes are shown in the left column of We analyzed biomarker relationships with mixed-effects modeling (MEM) consisting of a fixed component for the average population trend and a random component for individual deviation from the population mean. MEM controlled for the dependence between biomarkers obtained from eyes of the same person by incorporating inter-eye correlation into the model. All possible pairwise biomarker combinations were analyzed by linear MEM with random intercepts to account for the clustering within participants. Biomarkers from the H and V b-scans were treated separately. For each biomarker pair, we selected the best among the quantitative models in Model performance was assessed through two coefficients of determination: the marginal coefficient . A base level of 0.05 was used for all significance testing.V) were tightly coupled to their defect counterparts (DV), unsurprising given that they are related through the age-adjusted normal HOV which varies slowly and is nearly constant in the central field [V30 and DV30, respectively. The list of all significant function-function and structure-structure associations appears in Functional measures of the patient’s HOV  HOV slope distances V Click here for additional data file.S2 Table-2 = inverse quadratic, X = linear, and X2 = quadratic. Significant p-values are in bold.Listed for each structure-function relationship are, in order, the best-fit model abbreviation preceded by the correlation direction (+ or -), (DOCX)Click here for additional data file.S3 Table-2 = inverse quadratic, X = linear, and X2 = quadratic. Significant p-values are in bold.Listed for each structure-function relationship are, in order, the best-fit model abbreviation preceded by the correlation direction (+ or -), (DOCX)Click here for additional data file.


Why this happened remains unclear, in part because of difficulties measuring the C4 cycle in situ in C3-C4 intermediate species. Now, 4 cycle activity, thereby providing the means to analyze its upregulation in an evolutionary context.Upregulation of the C Next, restrictions of glycine decarboxylase activity to the BS creates a photorespiratory glycine shuttle that concentrates CO2 into the BS, enhancing Rubisco efficiency (in what is termed C2 photosynthesis). The C4 metabolic cycle is then upregulated, beginning with enhancement of PEP carboxylase (PEPCase) activity, and following a series of optimizing adaptations, an efficient, fully functioning C4 pathway is created. To the left of the diagram, the Flaveria species that correspond to specific evolutionary stages are shown, with those studied by The diagram shows a conceptual model of how the C2 photosynthetic bridge to C4 photosynthesis, it is only the first half of the bridge. The second half involves the upregulation of the C4 metabolic cycle. Why C4 metabolism became upregulated is unknown, although a recent hypothesis suggests it happened to provide carbon skeletons for re-assimilation of ammonia released by BS glycine decarboxylase , the model genus for studying C4 evolution. The four species differ in degree of C4 cycle engagement: F. bidentis is a full C4 plant; F. pringlei is a C3 species with no C4 cycle activity; and two species are C3-C4 intermediates, one with a modest C4 cycle to compliment the dominant C2 cycle (F. floridana), the second with a strong C4 cycle but retaining slight Rubisco expression in the mesophyll cells .Using a method that involves real-time observations of steady-state 4 cycle activity in C3-C4 intermediates is that the C4 cycle operates in parallel with a C2 cycle , such that the biochemical signatures of their respective activities are difficult to segregate. Historically, the relative contribution of the C4 cycle to carbon gain was assessed using pulse-chase experiments to determine 14C incorporation into the initial metabolites fixed by PEP carboxylase and Rubisco, a time consuming procedure that required sample destruction and chromatographic separation of radioactive compounds  and ammonia (NH3). The CO2 in the BS can then accumulate to levels two to three times that in the mesophyll cells and stimulate Rubisco activity in nearby BS chloroplasts, while the ser returns to the mesophyll cells to be metabolized back to RuBP in a series of steps. A C4 metabolic cycle can also function in species conducting C2 photosynthesis, to provide additional CO2 to the BS, but also possibly to provide carbon skeletons to facilitate NH3 reassimilation in the BS, as indicated by the dashed line in the BS cell (The diagram summarizes the photorespiratory glycine (gly) shuttle, showing how glycolate (glc) produced after Rubisco (R) oxygenation of RuBP in the mesophyll cells diffuses into the BS cells where the glycine decarboxylase (GDC or G)-containing mitochondria can metabolize it to CO3-C4 intermediacy by measuring reductions in the CO2 compensation point of photosynthesis (Γ); however, it cannot delineate C4 cycle contributions because Γ is affected by glycine shuttling and the C4 cycle (13C) can identify C4 cycle activity, because PEP carboxylase discriminates against 13C less than Rubisco. This leads to the well-known difference in δ13C between C3 and C4 plants, where the δ13C of C3 plants is –22‰ to –32‰ while in C4 plants it is –9‰ to –16‰. This difference is easily detectable with a mass spectrometer, which has been valuable for screening C3 to C4 transitions in phylogenetic clades using plant material from herbarium collections . To avoid these complications, real-time, mass spectroscopy should be coupled to gas exchange analyses, producing ‘on-line’ carbon isotope assessments that reflect the immediate biochemistry of CO2 fixation. On-line measurements are facilitated by tunable-diode laser absorbance spectrometers (TDLASs), which are best known from mesophyll conductance studies  against 13C by photosynthesis. The C4 versus C3 cycle activity can then be determined by simultaneously measuring and model-fitting CO2 exchange and ∆ responses to variation in atmospheric CO2 and O2, as described by 3, C3-C4 intermediate and C4 photosynthesis, and incorporates contributions from mesophyll conductance and transpiration rate. This is important, because CO2 provided to BS Rubisco by glycine decarboxylase increases ∆, while CO2 provided by PEP carboxylation and the C4 cycle decreases ∆, such that the two signals offset. Through their approach, Alonso-Cantabrana and von Caemmerer overcome this conflict to reveal the C4 cycle contribution.The on-line process factors out variation in source gas δ4 cycle activity at current atmospheric CO2 levels contributes about 12% of the carbon assimilated in F. floridana and 80% of the carbon assimilated by F. brownii , which is comparable to pulse-chase estimates using 14C , the C4 cycle contribution increases to 15% in F. floridana and 90% in F. brownii. At high CO2, the C4 contribution dropped off in F. brownii, to only 75%, reflecting a marked increase in the efficiency of the residual Rubisco left in its mesophyll tissue. This increase in the contribution of Rubisco to CO2 fixation causes a substantial rise in the biochemical ∆ from below one at low CO2 to near six at high CO2 . Moreover, the ability to predict the CO2 response of photosynthesis in the intermediates was much improved by incorporating their estimated C4 cycle contribution, as was the modelled CO2 response of ∆.With their approach, 4 cycle activity can improve carbon gain at low CO2 levels, yet become less significant at elevated CO2 if a modest amount of Rubisco is present in the mesophyll tissue. In F. floridana, the function of the C4 cycle has been questioned, since it seemed to contribute little to photosynthesis, and thus was suggested to initiate a futile cycle (4 cycle does indeed contribute to CO2 fixation, and thus is not futile and could be adaptive in low CO2 conditions of recent geological time, when atmospheric CO2 fell below 200ppm (These responses highlight how small amounts of Cw 200ppm .3-C4 intermediates from a range of lineages, thereby enabling comparative analyses for addressing hypotheses explaining how evolution upregulated C4 metabolism. When coupled with genomic and ecological data, C4 researchers should now be able to evaluate in detail how one of the most evolutionary complex traits on Earth repeatedly evolved in recent geological time.In summary, Alonso-Cantabrana and von Caemmerer have provided researchers with a powerful approach that can be quickly applied to many C


Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner.Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project.We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge.The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer. Cancer clinical outcome prediction using gene expression profiles has been proposed by the field of translational bioinformatics for better diagnostics, prognostics, and further therapeutics . SomaticHowever, clinical phenotype prediction based on gene expression profiles can vary between independent data sets ,7. One pP21 and can be activated by several pathways such as MAPK pathway. In addition, protein family interactions are essential to the functioning of individual cells in several ways through either domain-domain interactions or inter-chain protein interactions [In general, most pathway analysis approaches assume that each pathway is independent of other pathways . Howeverractions ,17. ThusIn this study, we demonstrate a novel approach for identifying knowledge-driven genomic interactions associated with cancer stage using grammatical evolution neural networks as part of the Analysis Tool for Heritable and Environmental Network Associations (ATHENA) software . A knowlth leading cause of cancer mortality in women in the United States [In order to test the utility of the proposed approach, we used ovarian cancer data from the Cancer Genome Atlas (TCGA). Ovarian cancer has the highest mortality among gynecological malignancies, and is the 5d States . Patientd States . Thus, ihttp://tcga-data.nci.nih.gov/). All samples in ovarian cancer from TCGA met broadly accepted quality control standards, including NUSE IQR, percentage present, and GAPDH 3’/5’ ratio [stage I or stage II, whereas ‘late stage’ indicates patients diagnosed either stage III or stage IV. A total of 39 patients were classified as early stage and 454 patients were classified as late stage.Normalized gene expression data from Affymetrix HT Human Genome U133 Array Plate Set in ovarian cancer was retrieved from the TCGA data portal  , Gene OnWe used ATHENA, a multi-functional software package, to perform the three tasks: (1) performing feature/variable selections from categorical or continuous independent variables; (2) modeling main and interaction effects that explain or predict categorical or continuous clinical outcomes; (3) interpreting the significant models for use in further translational bioinformatics ,30. The http://ritchielab.psu.edu.We have extended ATHENA to identify knowledge-driven genomic interactions that explain the multi-layered architecture of complex traits. Figure In order to identify non-linear interactions between genomic features, stochastic methods employing evolutionary computing approaches utilize the full dimensionality of the data without exhaustively searching all possible combinations of variables that influence complex traits ,31,32. A1. The original dataset is divided into 5 equal groups for 5-fold cross-validation (4/5 for training and 1/5 for testing dataset).2. Training begins by generating a random population of binary strings initialized to be functional ANNs. The total population is divided into demes as sub-populations across a user-defined number of CPUs for parallelization.3. The ANNs in the population are evaluated using the training data and the fitness  for each model is recorded. A new population is generated as the solutions with the highest fitness are selected for crossover and reproduction.4. Step 3 is repeated for a pre-defined number of generations. Migration of best solutions occurs between demes every n-number of generations, as specified by user.5. The overall best solution across generations is tested using the remaining 1/5 test dataset and fitness is recorded.6. Steps 2–5 are repeated four more times, each time using a different 4/5 of the data for training and 1/5 for testing. The best model is defined as the model identified the most over all five cross-validations.knowledge-based datasets. First, genes in gene expression data were mapped to pathways. Second, genes found within a concomitant pathway were grouped together and this process was repeated for all genes/pathways. Third, for each patient, each set of genes becomes a new matrix feature by summing up gene expression values across genes belonging to each pathway. Last, for each set of genes, the value of the pathway feature was divided by the total number of gene members in a pathway because the values of pathway feature can be biased toward the total number of gene members belonging to each pathway. The left side of Figure Figure knowledge integration knowledge-driven models. The balanced accuracy, which avoids inflated performance estimates on imbalanced datasets, was used in GENN as a fitness function.After the transformation step, we analyzed the knowledge-based datasets to generate the best predictive model using GENN. Finally, we integrated the best model from different knowledge-based datasets to develop a model associated with ovarian cancer stage to form The gene expression data and knowledge-based data after the transformation step were analyzed separately by GENN with the following parameters, population sizes of 25,000 per deme, 20 demes (populations), 300 generations, and migration every 15 generations. To establish a baseline evaluation of the performance of gene expression data alone using GENN for model development, we found the best balanced accuracy value from the testing cross-validation set for each of the models with gene expression alone was 69.57%. However, when we used the knowledge-driven approach, we found an improvement in balanced accuracy. For the KEGG pathway-based matrix, GO-based matrix, and Pfam-based matrix the highest balanced accuracy increased to 74.52%, 69.92%, and 70.45%, respectively  activates the transcription of genes that are involved in crucial aspects of cancer biology, and is well known as a cancer drug target for several cancers [FAK), a part of the focal adhesion pathway, plays a functionally significant role in ovarian cancer migration and invasion [HIF-1 expression [Two pathways, the “HIF-1 signaling” pathway and the KEGG “focal adhesion” pathway, were found in the GENN models associated with ovarian cancer stage. Hypoxia-inducible factor-1 ( cancers ,34. In ainvasion . These tpression . In addipression .Even though models from KEGG pathway-based datasets and GO-based datasets showed linear additive effects, the models from Pfam-based dataset showed complex and non-linear interactions between protein family features associated with stage. The non-linear interactions of protein families, “annexin”, “leucine rich repeat N-terminal domain”, “BTB/POZ domain”, and “ephrin receptor ligand biding domain”, might contribute to the stage in ovarian cancer rather than any single protein family. The annexin protein family has been shown to be associated with cisplatin resistance and related to tumor recurrence in ovarian cancer ,39. As aLRRC3B, encoding a Leucine-rich repeat-containing protein, is known as a putative tumor suppressor gene in gastric cancer and might be associated with cell surface binding peptides [In the final model, where we used multiple knowledge sources, HIF-1 signaling from KEGG, the GO term “Cell surface binding”, and the Pfam “Leucine rich repeat N-terminal domain” were selected. Complex interactions of biological knowledge might reflect the complex molecular pathogenesis and progression of ovarian cancer. Notably, peptides .In this study, we integrated biological knowledge to overcome the variability of diagnostic predictors across gene expression datasets and to increase the predictive power of gene expression data. Pathways co-operate together, and protein families are likely to interact with each other rather than acting in isolation, thus it is desirable to incorporate genomic knowledge for effective modeling and prediction of cancer clinical traits and outcome. Herein we proposed a novel approach for identifying knowledge-driven genomic interactions associated with cancer stage using GENN. GENN have been shown to be powerful in genetic association studies and meta-dimensional analysis of phenotypes of interest and have been shown to be successful when compared to other methods in term of prediction accuracy ,30,32,42HIF-1 expression. In terms of accuracy, the knowledge-driven genomic interaction model outperformed the model with gene expression data alone. In addition, the results from the model were more interpretable because of the biological context of pathways. Genomic features in the same process such as signaling pathway or metabolic pathway are likely to operate together in cancer, and our modeling approach allows for models that reflect these pathways and complex interactions.In order to demonstrate the utility of the proposed approach, ovarian cancer data from TCGA was used as a pilot project. Through the proposed approach, we identified not only knowledge interactions in a same knowledge source such as pathway-pathway interactions but also knowledge interactions among different sets of knowledge such as pathway-protein family interactions by integrating different types of knowledge. Cooperation of the “HIF-1 signaling” and “focal adhesion” pathways is thought to be important in the mechanisms of complex tumorigenesis in ovarian cancer because focal adhesion kinase signaling pathway regulates the proliferation and migration of choroidal microvascular endothelial cells by acting through etc. This methodology can be applied to other cancer types in order to identify the cancer-specific or common interactions among cancer types as well as other common, complex diseases. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view on interactions within/between different biological knowledge could provide more effective screening strategies and therapeutic targets.One of the limitations in this study is that the current implementation of GENN is to select the best model in the final solution because it has higher accuracy than all of the other models during the cross validation procedure. However, there might be multiple different good models and selection based on accuracy alone has its limitations. To challenge this limitation, incorporation of Pareto optimization can be alternative in the next iteration of GENN. Pareto optimization is a multi-objective optimization method that aims to maximize or minimize multiple objectives, allowing us to find multiple interactions in cancer. In addition, assessing accuracy for an independent validation set using the best model trained from the cross validations should be performed in order to estimate a true validation error. Implementing a permutation test for assessing statistical significance could be also one of the future works to overcome the current limitation from the cross validation. Furthermore, multi-omics data including SNP, CNV, methylation, miRNA, and gene expression data can be inputs for ATHENA in order to determine the meta-dimensional models of complex disease. Integrating multi-omics data and biological knowledge will be a future direction. Further, while our current study was limited to the classification of early stage or late stage in ovarian cancer, our proposed approach can be applied to other clinical outcomes such as survival, recurrence, metastasis, grade, The authors declare that they have no competing interests.DK and MDR designed and developed the study and wrote the manuscript. DK, RL, SMD, ATF, and SAP provided the experimental results and interpreted the results. MDR provided intellectual guidance and mentorship and wrote the manuscript. All authors read and approved the final manuscript.


Homo sapiens targets were identified for fifteen GTPs. These targets were classified into six groups according to their related disease, which included cancer, diabetes, neurodegenerative disease, cardiovascular disease, muscular disease, and inflammation. Moreover, these targets mapped into 143 KEGG pathways, 26 of which were more enriched, as determined though pathway enrichment analysis and target-pathway network analysis. Among the identified pathways, 20 pathways were selected for analyzing the mechanisms of green tea in these diseases. Overall, this study systematically illustrated the mechanisms of the pleiotropic activity of green tea by analyzing the corresponding “drug-target-pathway-disease” interaction network.During the past decades, a number of studies have demonstrated multiple beneficial health effects of green tea. Polyphenolics are the most biologically active components of green tea. Many targets can be targeted or affected by polyphenolics. In this study, we excavated all of the targets of green tea polyphenolics (GTPs) though literature mining and target calculation and analyzed the multiple pharmacology actions of green tea comprehensively through a network pharmacology approach. In the end, a total of 200  Among all teas consumed worldwide, green tea is the best studied in terms of health benefits because its chemistry is more well known than that of other teas . A numbe (−)-EZG , (+)-CG  (−)-ECG , (+)-GCG(−)-EGCG , and two(−)-ECMG  and (−)-−)-EGCMG , accountNetwork pharmacology updates the research paradigm from the current “one target, one drug” model to a new “multicomponent, network target” model, which enhances our knowledge of multipathway interactions and helps interpret drug-response signature datasets that collectively decode the complex mechanisms of drug actions . Unlike  Homo sapiens targets were identified for fifteen GTPs. These targets were classified into six groups according to their related disease, which included cancer, diabetes, neurodegenerative disease, cardiovascular disease, muscular disease, and inflammation, in agreement with the applications of green tea. Pathways analysis was used to illustrate the mechanism of action through which the GTPs exert their comprehensive therapeutic effects. A total of 143 KEGG pathways were identified, and 26 of these were more enriched, as determined through pathway enrichment analysis and target-pathway network analysis. Twenty of the identified pathways that play a role in the GTP-related diseases were selected for analyzing the mechanisms of action of green tea. By integrating the “drug-target-pathway-disease” interactions, we found the green tea exerts a variety of therapeutic effects though the modulation of multiple pathway by its main components, which is in accordance with the “multicomponent, network target” model.To explore the mechanism of action of green tea from a holistic perspective, the network pharmacology method was employed to investigate the molecular behavior of GTPs. We collected all available target information for fifteen GTPs through literature mining and computational chemistry (3D similarity search and reverse docking) to construct a network of the “drug-target-pathway-disease” interactions . In the All available information on the targets of fifteen GTPs in the literature was collected by searching PubMed using the Structure search. Only the confirmed and active targets were selected from the category of biological test results.The online web server ChemMapper , which iReverse docking is a novel technology that allows the docking of a compound with a known biological activity into the binding sites of all of the 3D structures in a given protein database. The PharmMapper server is a freely accessed web server designed to identify potential target candidates for a specific small molecule probe  using the pharmacophore mapping approach . It is bhttp://www.cytoscape.org/) and the Network Analyzer plugin  were used to visualize the network and calculate the basic network parameters, including degree of distribution, degree exponent, shortest-path-length distribution, and clustering coefficient. The interactions in the “target-pathway” network were selected from the pathway enrichment results. The sizes of the nodes correspond to the node degree.The individual interaction networks for each protein were built by use of the STRING database which is integration of known and predicted protein interactions . The netP values were used to determine if a specific pathway in the KEGG database was more enriched with the related proteins than by chance. Assuming that a total of K proteins related to the GTPs were mapped into KEGG, which contains N distinct proteins, and k proteins from a pathway of size n are related to the GTPs, the P value is given by sapiens targets, 200 Homo sapiens targets were selected for the subsequent analysis .The 15 GTPs shown in  Homo sapiens targets, 120 targets were related to cancer, which is in agreement with previous research results showing that green tea has anticancer activity [ 13 (kaempferol), 14 (quercetin), 6 ((−)-EGCG), and 15 (quercetin), which exhibited node degrees greater than 40. Simultaneously, most of the targets can be targeted by more than one GTP.The network of drug-target interactions is shown in activity . In addi via mapping the 200 putative targets into the String database, which is a collection of known and predicted protein-protein interactions. After excluding isolated nodes, the protein interaction network induced by green tea components was composed of 187 nodes (proteins) and 1500 edges (interactions) , the EGFR/FGFR/IGFR-MAPK signaling pathway  and other related downstream signal transduction pathways. In addition to apoptosis, the final impact of these targets in a cancer cell is mainly associated with angiogenesis, proliferation, and genomic damage. Importantly, most of these targets or pathways, such as Bax [κB [To fully elucidate the molecular mechanisms in cancer prevention, we collected all of the potential and confirmed targets of the GTPs to identify all of the related pathways. In the end, 12 pathways in cancer were identified through pathway enrichment analysis . To betth as Bax , NF-κB [ Bax [κB , MAPK pa Bax [κB , EGFR [2 Bax [κB , IGFR [2 Bax [κB , and COX Bax [κB , have beP values of 2.39 × 10−4 and 1.68 × 10−3, respectively. Even though the P value found for the type II diabetes mellitus pathway was less than the set significance level, it still was selected for further analysis because it is closely correlated with diabetes. We constructed a diabetes-related pathway  is a progressive neurodegenerative disorder that is pathologically characterized by the deposition of β-amyloid (Aβ) peptides as senile plaques in the brain. EGCG has been proven to reduce Aβ generation [β-site APP cleaving enzyme 1 (BACE), which promotes the formation of Aβ by cleaving APP, can be regulated by the GTPs. The membrane metalloendopeptidase (NEP), which inactivates the degradation of Aβ, can also be targeted by the GTPs. Evidence from several studies has indicated that the hyperphosphorylation of the Tau protein is responsible for its loss of biological activity and its resistance to proteolytic degradation and likely plays a key role in neurofibrillary degeneration in AD [Neurodegeneration, which occurs in Parkinson's, Alzheimer's, and other neurodegenerative diseases, appears to be multifactorial, in which a complex set of toxic reactions, including oxidative stress (OS), inflammation, reduced expression of trophic factors, and accumulation of protein aggregates, lead to the demise of neurons. One of the prominent pathological features is the abnormal accumulation of iron on top of the dying neurons and in the surrounding microglia . Tea flaneration . Moreoveon in AD , 40. BasPrevious epidemiological, clinical, and experimental studies have established a positive correlation between green tea consumption and cardiovascular health. Catechins have been proven to exert vascular protective effects through multiple mechanisms, including antioxidative, antihypertensive, anti-inflammatory, antiproliferative, antithrombogenic, and lipid lowering effects . TherefoκB signaling pathway . Moreover, the key target Cdc42, which regulates the actin cytoskeleton, can be affected by compounds 4 and 5, which may promote the differentiation and migration of muscle cells.Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disease that leads to early disability and death . The disκB pathways and STAT signaling [β, and NFκB) that contribute to the immune response can be modulated by GTPs .The anti-inflammation activity of green tea has been demonstrated in many studies. Previous research has mainly focused on EGCG, which can disturb many inflammation-related pathways, such as the MAPKs, AP-1, and NFignaling . In addiGreen tea is commonly associated with traditional beverage rituals and particular lifestyles and is currently considered a source of dietary constituents endowed with biological and pharmacological activities that result in potential benefits to human health. Indeed, the novel pharmacological activities of green tea are arousing interest in the possible clinical use of green tea components for the prevention and treatment of several diseases. Although numerous studies are attempting to elucidate the molecular mechanisms through which green tea exerts its diverse biological activities, particularly its anticancer activity, a systematic understanding of the mechanisms through which green tea reduces disease risk is necessary to establish its efficacy for the population for which it could be most useful. The favorable properties of green tea have been ascribed to the high content of polyphenolic flavonoids. It is possible that different GTPs act on different targets in the signaling network of complex disease, resulting in a synergistic effect, which is in accordance with the holistic philosophy of network pharmacology that follows the “multicomponent, network target” model. Therefore, to systematically and comprehensively understand the mechanisms responsible for the diverse biological activities of green tea, a network pharmacology approach was used to analyze the “drug-target-pathway-disease” interaction network, which are constructed by collecting all of the confirmed and potential targets of GTPs. Six classes of diseases experienced beneficial therapeutic effects by green tea, as was determined at the molecular and signaling pathway levels based on the interaction network, which provides a better understanding of how the multiple ingredients in green tea act in synergy and the effects that these can have on multiple targets of a disease.Moreover, this research study provides comprehensive and useful data for more in-depth studies of mechanisms of action of green tea. In addition, the understanding of the cell signaling pathways and the molecular events leading to a therapeutic effect will provide further insight for the identification and development of potent agents that specifically target these pathways.Figure S1: Node degree distribution of the protein interaction network induced by GTPs.Figure S2-S4: GTPs mediated pathways in cardiovascular diseases, muscular disease and Inflammation, respectively.Table S1: 200 Homo targets and interactions of “GTPs-Targets-Disease”.Table S2: All pathways identified by pathway enrichment analysis and related targets/genes.


Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.The online version of this article (doi:10.1186/s12918-014-0121-3) contains supplementary material, which is available to authorized users. Precision medicine requires of better ways of defining diseases by introducing state-of-the-art genomic technologies into the diagnostic procedures. A more precise diagnostic of diseases, based on the description of their molecular mechanisms, is critical for creating innovative diagnostic, prognostic, and therapeutic strategies properly tailored to each patient’s necessities . ConsequNevertheless, functional modules with detailed information on their internal structures are available in several repositories such as KEGG , ReactomIn most of the methods described above individual values accounting for gene expression are combined, as defined by the internal structure of the pathway, into a global score for the entire pathway. That is, pathways are used as whole functional units in the interpretation of gene expression experiments. However, the biological consequence of the activity of a pathway is not a trivial concept. Stating that a pathway is activated (or deactivated) is not very informative by itself. In fact, partial activation (or deactivation) within the same pathway can have very different (and sometimes opposite) biological implications. For example, the apoptosis signaling pathway can lead to two opposite cell behaviors: apoptosis and degradation (cell death) or antiapoptosis  depending on the final protein that receives the signal (see hsa04210 pathway in the KEGG repository). Therefore, depending on their relative location within the pathway, changes in the activation state of one or several proteins can change (and even reverse) the cell behavior. Some of the PT-based methods use values of differential gene expression within the context of the pathways to calculate scores for pathway sub-structures -31,36. HFrom a completely different angle, other approaches have attempted to model protein signaling networks in detail -40. SuchThe approach proposed here offers a conceptually new way of understanding the functional consequences of changes in gene activity associated to any comparison of conditions by taking into account the internal structure of the pathways. This structure is used to split up the pathway into stimulus-response sub-pathways, or circuits, which trigger particular cell functionalities. In other words, the method enables the estimation of the probabilities of activation of these cell functionalities. This approach allows overtaking the simplistic view of a pathway as an entity that is either active or inactive as a whole. Here we show how the analysis of these signaling circuits results in a more detailed and realistic description of the functional consequences of gene up-and down-regulations within the context of each pathway. In many cases, the resulting activation/deactivation of individual functionalities within the pathway helps to suggest a causal explanation for disease mechanisms. We illustrate the application of the methodology with gene expression data obtained from various microarray experiments with different conditions and diseases.As previously commented pathways are multifunctional entities that can trigger a range of different cell functions and it is often meaningless to talk about the activation of a pathway as a whole or relating its global activity to a disease. Opposite to this view, we propose a different method which accounts for the change in the probabilities of signal transmission along the different signaling circuits defined within pathways. Briefly, we first use the values of gene expression to infer probabilities of gene product presence/absence . Then, we use such probabilities to calculate the probability of signal transmission along the circuit, from the receptor protein (that receives the stimulus) to the effector protein (that triggers the response). We take into account that interactions between proteins can be either activations or inhibitions . All the samples would represent pathways whose genes will have a similar activity state and, consequently, should have their signaling circuits in the same activity state. For this reason, no differences should be observed in any comparison. We produced 1000 datasets, of n = 10, 20, 50 and 100 samples with probabilities 0.1, 0.3, 0.5, 0.7 and 0.9 of being activated. Figure We have also used a large dataset of pediatric acute myeloid leukemia (AML) which contains gene expression microarray data of 237 children . In prinAdditionally, two microarray datasets containing cases and controls in a study of breast cancer (GSE27562)  and anotJak-STAT pathway  to illustrate this concept. A recent microarray (Affymetrix™ array HG-U133 Plus2.0) study has been used to systematically search for genes differentially expressed in early onset CRC . The stuWNT signaling pathway. This pathway has a canonical Wnt/β-catenin cascade and two non-canonical pathways named Wnt/Planar cell polarity (Wnt/PCP) pathway and Wnt/Ca2+ pathway, forming part of the complete WNT signaling pathway. The results of the test reveal a significantly increased activity for two out of the three circuits  whose expression is up-regulated in tumor cells by cyclooxygenase-2 (COX2), represented in the KEGG pathway in Additional file PTGS2 (prostaglandin G/H synthase and cyclooxygenase), and directly correlated to neoangiogenesis and clinical outcome [COX2 catalyzes the production of PGI2, and high levels of PGE2 have been reported in colorectal adenocarcinomas in comparison with adjacent normal tissue [PTGS2 (COX2), was significantly activated  population during aging contribute significantly to the manifestation of these age-associated hematopoietic pathologies in mice .AURKA, FOS, HOXA9, MYC, TRIM13, while MAFF and FPLT3 resulted to be age-down-regulated [FOS gene in the T-Cell receptor signaling pathway. This circuit triggers proliferation, differentiation and immune response , a disease in which signaling is known to play a relevant role. FA is a rare chromosome instability syndrome characterized by aplastic anemia as well as cancer and leukemia susceptibility . It has FANCC, is involved in Jak/STAT signaling and apoptotic signaling [Jak/STAT pathway specifically triggers one of the survival circuits of the apoptosis pathway that eventually results in the disease. Figure CIHS, leading to cell proliferation, is inhibited the activity of the circuit ending in AKT3 is significantly activated in FA  and other three circuits are significantly active . The consequences of the observed changes in the activation status of the circuits affected can easily be understood at the light of the functions they trigger  is inactivated in FA. Additionally, another two circuits triggering degradation (DFFA) and cleavage of caspase substrate (CASP6), respectively, are also deactivated. On the other hand, circuits ending in BCL2L1, BIRC2 and BCL2, that trigger survival, are activated. Thus, the final consequence is that the antiapoptotic pathway activity in FA increases with respect to a normal cell. This does not mean that apoptosis cannot occur in FA. Actually it does [BAX and/or TP53 -based methods of functional enrichment, even if they take into account the relationships between the components, fail in revealing details on the molecular mechanisms that mediate the pathologic behaviors of the apoptosis in FA. And, obviously, cannot account for the cross-talk between pathways.Cocaine addiction, African trypanosomiasis, Long-term depression, Salivary secretion, and many others completely unrelated to cancer are obvious false positives. Focusing only on cancer related pathways we consider VEGF, Jak-STAT, ERBB and WNT. In VEGF, clipper only detect a portion of the sub-pathway leading to the production of COX2 via PTGS2 (prostaglandin G/H synthase and cyclooxygenase), which is detected by our approach and is known to be activated in CRC [It is difficult to provide a reasonable comparison of the procedure proposed with other procedures because they test different aspects of the functionality of the pathway. A recent revision on pathway-based methods describe the evolution of the different algorithms proposed from those that only consider membership relationships between the genes of a pathway  to those, more sophisticated, that use the topology of the pathway (PT-Based) . Howeverd in CRC ,68. ClipAdditionally, two almost identical approaches that define sub-pathways in a similar manner to our signaling circuits but score them in a similar manner to SPIA  have recIn an apparent conceptual downgrade, the way in which new sub-pathway based methods check its validity implies checking whether the pathways in which they found significant sup-pathways are also significant in conventional FSC or PT-based methods or not. Although probably of limited value, we produced a similar comparison. We have used the CRC example  for the No main discrepancies were observed among the significant values reported by the SPIA method and the proposed method. In some cases, the behavior of SPIA is closer to the GSEA, probably because both methods return a global pathway score  However, our method is testing a different aspect of the activity of a pathway than SPIA and GSEA do. For example, a pathway known to be active in cancer is the VEGF signaling pathway, whose inhibition has been suggested as an anticancer therapy . SeveralBecause of the complexity of the cell, functional traits cannot be understood as the result of the action of only one or a few genes ,72. The Therefore, in a realistic scenario, the conventional paradigm that link disease to the failure of a particular gene needs to be changed for a systems-biology-oriented view -77, in wUnlike other conventional approaches, where either the activity of individual genes or the activity of the complete pathway was tested, this approach dissects the structure of the pathway and tests the activity of the stimulus-response signaling circuits. Actually, the notion of pathway is somehow an arbitrary concept based, in some cases, on historical or theoretical concepts that may include many different actual functional roles. The aim of the method proposed here is focusing on the actual elementary functionalities triggered by these circuits within the context of the pathway, whose failure  can be associated to the disease. There are only a few recently published methods that consider sub-pathways, however they present some problems that seriously limit its applicability and compromises the accuracy of the results provided -31. FirsThe analysis of type I and type II errors demonstrated the accuracy of the methodology proposed. The examples presented in this paper illustrate the use of the concept of the probability of stimulus-response circuit integrity for signal transmission in different scenarios including cross-talk between pathways.We have focused on gene expression data obtained from microarrays purely by practical reasons of availability: it is the most abundant genomic data publicly available. Obviously, gene expression values can be obtained by other methodologies, providing the data compared are in the same scale . In particular, it is likely that in a near future most of the gene expression measurements will be obtained from RNA-seq experiments . DiffereDetailed pathway models helps to understand the disease mechanism  as well affy library [limma library [All data used in this study was downloaded from the Gene Expression Omnibus (GEO), public repository of the NCBI . By the  library  of Bioco library  of Bioco library  platform library . The datGene expression data for the CRC analysis were retrieved from the GEO (GSE4107). Gene expression datasets for the Fanconi Anemia analysis were obtained from GEO (GSE16334) (Affymetrix HG-U133A Array). Gene expression datasets for the murine models of obesity were downloaded from GEO (GSE36669). Human bone marrow hematopoietic stem cells gene expression data were retrieved from GEO (GSE32719). Gene expression data of pediatric acute myeloid leukemia (AML) used in the validation of the methodology were retrieved from GEO (GSE17855). Gene expression data for breast cancer and data from the study of genes with increased expression in AML were also retrieved from GEO .The original GEO normalization of each dataset was used. Differential gene expression control versus case samples were carried out using the Limma library  from BioSignaling pathways offer an excellent framework for deriving models of biological activity because their functionality can be easily conceptualized: it simply consists on the potential of transmitting a signal from the protein that receives a stimulus  to the effector protein that triggers the action  in response to the stimulus. In practical terms, any node with no incoming interactions is defined as signal input node and any node with outgoing interaction is defined as a signal output node. Then, a sub-pathway is defined as a signal entry point and the collection of intermediate connecting nodes that links it to a signal output node. We will name circuits to these particular sub-pathways in order to distinguish them from any other possible sub-pathway that does not connect signal input to signal output nodes within the pathway. Thus, we recode each pathway into a collection of circuits. Any of these circuits represent a potential elementary functionality triggered by the corresponding signal output node. Some signal output nodes have specific annotations, included in the KEGG pathway definition, about the functionality triggered, although a number of them lack annotation. Figure Homo sapiens and 18 for Mus musculus belonging to the general categories Environmental Information Processing and Cellular Processes, which include important processes and systems such as Signal Transduction , Signaling Molecules and Interaction , Cell Growth and Death , Cell Communication (GAP junction and tight junction), Endocrine System  and Immune System , could be coded in this way.A total of 27 KEGG pathways for The information about the topology of each pathway was extracted from the corresponding KEGG’s XML files. The KEGG database provides information on the genes and their corresponding relationships, making it possible to reconstruct each pathway from its corresponding XML-formatted file (in KGML format - KEGG Markup Language). The Dijkstra's algorithm  is used Phosphorylation, Dephosphorylation, Indirect, Expression and Compound are initially considered activations, unless the contrary is specified. That is, if a relationship is labeled as phosphorylation, we consider that the signal is transmitted to the next node, however, if it is labeled as both, phosphorylation and inhibition, we consider that the relationship is a phosphorylation that is inhibiting the activity of the next node in the pathway. On the other hand, Ubiquitination, and Inhibition are considered inhibitions. When several proteins are linked by binding/association relationships, or are indicated as a group in the XML file, they are considered as a single node that need to be simultaneously activated to transmit the signal.Only two types of relationships are considered: activations and inhibitions. These are coded in the XML file, either directly or through a more detailed description of the mechanism by which the molecules perform the action. Thus, according to KEGG nomenclature, the labels Once a pathway is represented in this way, the probabilities of signal transmission from any entry point (stimulus receptor) in the input layer to any exit point (protein that triggers the response to the stimulus) in the output layer can be calculated, providing the activation state of the nodes connecting them are known. Moreover, changes in signal transmission probabilities when comparing different experimental situations can be tested.mixdist [As mentioned above, a node can be composed of only one protein, several alternative proteins or a complex of proteins. The probability of activity in any of these scenarios can be conceptually derived from the probability of activation of a protein. Commonly, the presence of the transcript(s) corresponding to a gene is taken as a proxy of the presence of the resultant gene product . We assumixdist , was usemixdist ,92). ForOnce such calculations are available for any of the probesets across all the microarrays studied, the distributions obtained can be used to estimate the probability of activation for this probeset in a different microarray ,92,93.The probability of a probe being activated given its expression level x can be calculated from the Bayes’ Theorem:where, according to the Law of total probability:That, in terms of our mixtures can be written as:0 = P(Inactive) and π1 = P(Active) are the proportions in the population for each distribution, that are calculated by the mixdist package, and p0(x) = p(x|Inactive) and p1(x) = p(x|Active) are the distributions that form the mixture.Where πAlthough the distributions derived from these particular datasets are only applicable to the particular microarray platforms used in this study , it is straightforward to produce similar distributions for the corresponding probesets of reference in other microarray platforms . Probesets mapping on multiple genes are discarded from the calculations.Given a microarray, the probabilities for each probeset of being activated can be used to derive the probabilities of gene activation (proxies for the corresponding protein activation probabilities) in the experiment studied. We use the 90% percentile of the distribution of the probeset activation probabilities as the value of probability of gene activation. The rationale behind this approach is to take the highest signal of hybridization of a probeset as an evidence of the existence of gene activity trying to avoid the most extreme values. In this way we choose a representative value for each gene  by meansNodes in the pathway are composed of one or more proteins. In the case of nodes of multiple proteins, these can be independent or can be part of a protein complex. When the node is composed by a single protein, the probability of node activity is the probability of activation of this protein. The second scenario corresponds to a node composed by more than one alternative protein. These are supposed to be redundant in its activity, meaning that the existence of only one of these proteins would be enough for the transmission of the signal. In this case, all the probes corresponding to all the genes in the node are taken together and the 90% percentile of the distribution of their activation probabilities is taken as the value of probability of node activation. The third scenario, corresponding to a complex of proteins, is slightly different because all the proteins are simultaneously necessary for its integrity. Unlike in the previous case, we consider that the activity of the node depends on the simultaneous presence of all their components. Consequently, we consider that the probability of having this type of node active is conditioned by the lowest probability of having any of the proteins of the complex active. The lowest of these probabilities will be the limiting factor for the integrity  of the node. Of course, the scenarios for the nodes are idealizations of a reality which may be much more complex, but they have demonstrated to represent a realistic enough scenario and to work in practical terms .a) having all the nodes connecting the input node to the output node active and b) having all the nodes that are inhibitors of nodes in the pathway in an activation state compatible with the transmission of the signal. When there are several possible ways (bi- or multi-furcations) to transmit the signal from the input node to the output node (linear sub-pathways), the formula of the probability of the union of several events can be used. Thus, once input and output nodes and the topology of the intermediate nodes has been defined for a particular circuit, and the probabilities of activation for each node in the pathway has been estimated (as described in the previous section), the probability of signal transmission across any circuit can be calculated as:As stated above, a signaling circuit is defined by an input node (the protein that receives the stimulus), an output node (the protein that triggers the response to the stimulus) and all the intermediate nodes connecting them. Thus, the probability of signal transmission along a circuit can be derived from the probability of n the number of linear paths conforming a circuit and being Ai  any of the paths in the circuit. Thus, the resulting probability, which can be assimilated to the probability of signal transmission across the circuit, is estimated as the probability of the union of all the linear paths that form this circuit.being Figure Circuits containing loops cannot be modeled under this simple approach and are not considered here. Specifically, this situation occurs in four pathways where we eliminated the following number of circuits .Since we are using gene expression values as proxies of protein activation statuses, the resulting probabilities of signal transmission across circuits at mRNA level might not have an exact correspondence with the protein level. In other words, the value of probability obtained for a circuit could not be by itself very informative about the real activation status of the circuit. However, the comparison of two conditions will render results with a clear biological interpretation: a significant change in the probability of signal transmission along a circuit is most probably accounting for a real change it the cellular response to any stimulus. In that case it can be anticipated that changes in mRNA levels causing significant changes in signal transmission circuit status will most likely be accompanied by the corresponding change at protein levels.wilcox.test function) to compare the difference of activation probability of each individual circuit in the pathway. Then we use a False Discovery Rate (FDR) [wilcox.test function returns the location parameter that indicates which is the activated condition when a circuit was found to be significantly activated. Depending on the topology of the pathway a few genes can produce a remarkable change in the number of circuits activated/deactivated or vice versa.This method seeks to assess the activation probability of every circuit, representing a canonical functionality within the pathway, given the status of the sample  In order to achieve that, we use the Wilcoxon test  (implemete (FDR)  control In order to compare the proposed approach to other pathway-based methods we have used different algorithms for functional analysis, namely singular enrichment analysis (SEA), gene set enrichment analysis (GSEA), and impact analysis . The FatIn order to test the sensitivity of the method for the estimation of the probabilities of circuit activation, we have used them as features to predict disease class. A low misclassification rate can be considered an appropriate proxy for a low type II error rate ,35. We hhttp://pathiways.babelomics.org. The results are displayed in a table that contains each circuit and its corresponding p-value, its FDR corrected p-value and its estimation of the location parameter. A graphical representation of the results in the pathway context of a KEGG-like map is also provided. This representation is obtained using the R package igraph [A web interface that implements the test described above has been developed  and is ae igraph . Nodes b


Adverse drug reactions (ADRs) are responsible for drug candidate failure during clinical trials. It is crucial to investigate biological pathways contributing to ADRs. Here, we applied a large-scale analysis to identify overrepresented ADR-pathway combinations through merging clinical phenotypic data, biological pathway data, and drug-target relations. Evaluation was performed by scientific literature review and defining a pathway-based ADR-ADR similarity measure. The results showed that our method is efficient for finding the associations between ADRs and pathways. To more systematically understand the mechanisms of ADRs, we constructed an ADR-pathway network and an ADR-ADR network. Through network analysis on biology and pharmacology, it was found that frequent ADRs were associated with more pathways than infrequent and rare ADRs. Moreover, environmental information processing pathways contributed most to the observed ADRs. Integrating the system organ class of ADRs, we found that most classes tended to interact with other classes instead of themselves. ADR classes were distributed promiscuously in all the ADR cliques. These results reflected that drug perturbation to a certain pathway can cause changes in multiple organs, rather than in one specific organ. Our work not only provides a global view of the associations between ADRs and pathways, but also is helpful to understand the mechanisms of ADRs. Adverse drug reactions (ADRs) are undesired phenotypic effects that occur in human organisms after medicine administration. In recent years, ADRs have gained broad public attention. ADRs not only affect quality of life for patients, but also have become a major cause of death. Furthermore, ADRs concern the economical profit of the pharmaceutical industries. ADRs are responsible for drug candidates' failure to gain FDA approvals during clinical trials. Many marketed drugs have been withdrawn due to severe ADRs. For example, Cerivastatin was withdrawn from the world market, because this drug caused 52 deaths that were attributed to rhabdomyolysis and kidney failure . Therefo in silico docking, which was based on information in the publicly available Protein Data Bank, and identified ADR-related pathways using logistic regression ” qualifier was applied in the PubMed search. Second, because pathway terms are noun phrases, it is not easy to detect pathway terms in the literature through a simple exact string match. We extracted pathway terms using a text-mining tool PathNER which used soft dictionary matching and rules to identify pathway terms in the literature . Of 1,69P ≤ 0.01). To examine the ability of the pathway-based ADR-ADR similarity measure to quantify ADR relations, we calculated the closeness between drug sets of two ADRs . This result indicated drug sets of similar ADRs were closer than that of dissimilar ADRs, implying that our method can link similar ADRs to one pathway. Therefore, our method can efficiently identify the relations between ADRs and pathways.To further validate the inferred ADR-pathway associations, we proposed a pathway-based ADR-ADR similarity measure method using hypergeometric enrichment. Namely, two ADRs were similar when the pathways that were associated with the two ADRs significantly overlapped based on hypergeometric test (ADRs see . The bacHeart failure (HF) is a condition in which the heart is unable to pump out sufficient blood to meet the need of the body. The features of HF are systolic/diastolic dysfunction and impaired electrical conduction . Upon thP = 0.9617 (using igraph package in R). Like many other known biological networks, the AA network also showed scale-free characteristics.To better understand the mechanisms of ADRs, we constructed an ADR-ADR (AA) network in which two ADR nodes were connected if they were similar according to the pathway-based ADR-ADR similarity measure. The AA network contained 273 ADRs and 3,241 interactions  is a standardized medical terminology that is used for adverse event reporting in the USA, European Union, and Japan . MedDRA Finally, we used CFinder  to mine  in vivo of novel predicted ADR-pathway associations needs further investigation.It is worth noting that our work can only identify pathways that are affected by a certain amount of drugs. If a pathway is affected by a very few drugs, then it is unable to associate observed ADRs with this pathway. However, this will be alleviated through completing drug-target relations. With the development of cheminformatics, more precise associations between ADRs and biological pathways will be identified. Another limitation is that examinationIn this study, we proposed a new concept to identify the ADR-pathway associations using enrichment analysis based on multilevel relation. Compared with previous works, our method used existing drug-target relations. Therefore, our results do not depend on the selection and accuracy of various drug-target prediction algorithms or require structural information concerning compounds and proteins. Additionally, the network analysis method was used to systematically analyze ADR-pathway associations. The results showed that ADR-related pathways were valuable resources in elucidation of the mechanisms of ADRs and investigation of the concurrence of ADRs.The inferred ADR-pathway relations can be used to elucidate the mechanisms of drug side effects. To this end, we used hypergeometric test, which is a typical method for measuring the significance of the associations between two variables, to identify ADR-pathway associations. We evaluated the performance of the method by using a text-mining tool to obtain cooccurring pathway terms with ADRs and defining a pathway-based ADR-ADR similarity measure. The results suggested that our method can be efficient for identifying associations between ADRs and pathways. Then, we constructed the ADR-pathway network based on the identified ADR-pathway associations. This network contributes to the investigation of drug-activated biological pathways that regulate phenotypic changes in cells. For example, the pathogenesis of heart failure was likely attributed to three routes: apoptosis, gap junction, and MAPK signaling pathway. Topological property analysis revealed that, on the one hand, disturbance of most pathways led to concurrency of multiple ADRs and, on the other hand, many ADRs were caused through synthetically affecting multiple biological pathways. We also found that the degrees among the rare ADRs, infrequent ADRs, and frequent ADRs were significantly different. Frequent ADRs were associated with more pathways than rare and infrequent ADRs. To uncover the reasons for this phenomenon, we calculated the number of all drugs causing one ADR and the number of total targets of these drugs. Although frequency of one ADR is defined as the percentage of patients reporting the ADR after taking drug rather than the number of drugs with the ADR, we found that frequent ADRs were linked to more drugs and drug sets eliciting frequent ADRs were linked to more targets. We further investigated the contributions of pathway categories to ADR etiology. The results showed that EIP contributed most to the observed ADRs. Subsequently, an AA network was generated according to the pathway-based ADR-ADR similarity measure. After accessing the interaction frequency between any two SOCs, we found that SOCs frequently interacted with other SOCs rather than themselves, indicating that perturbation to a certain pathway may affect multiple system organs instead of one specific organ. It was further supported by investigating the SOC distribution in all the ADR cliques. These results can provide references for side effect assessment in drug design. When an ADR in one SOC presented after drug administration, the biological characteristics of the organs that frequently interacted with the SOC should be investigated. Finally, there is a new speculation concerning ADR cliques. ADRs in one clique shared the same pathway according to the construction method of the AA network. Then, after drug treatment, the corresponding pathway was affected by this drug. The ADRs in the same clique likely cooccurred. For example, for ADR clique 20, patients with rheumatoid arthritis experienced adverse events, including dyspepsia, pneumonia, and gastroenteritis after Tacrolimus treatment . AdditioSupplementary Figure S1: Statistics of the drug-ADR relations and the drug-pathway relations The number of drugs is counted for each ADR, and the number of drugs per ADR is plotted versus the number of ADRs. For example, there are 25 ADRs with 9 drugs. Similarly, the number of drugs per pathway is plotted.Supplementary Dataset S1: The ADR-pathway pairs in the ADR-pathway network.Supplementary Dataset S2: The ADR-ADR pairs in the ADR-ADR network.Supplementary Table S1: All cliques in the ADR-ADR network.