The proposed method uses gene expression data to evaluate the the collection of activated or suppressed signaling pathways in tissues or neurons of the cognitively enhanced brain. An algorithm maps expression data onto signaling pathways and quantifies their individual activation strength. The collective pathways and their activation form what we term the signaling pathway cloud, a biological fingerprint of cognitive enhancement (or any other condition of interest). Drugs can then be screened and ranked based on their ability to minimize, mimic, or exaggerate pathway activation or suppression within that cloud. Using this approach, one may predict the efficacy of many drugs that may enhance various aspects of cognition before costly preclinical studies and clinical trials are undertaken.The go-to cognitive enhancers of today are those that are widely available rather than optimal for the user, including drugs typically prescribed for treatment of ADHD  and sleep disturbances such as narcolepsy (modafinil). While highly effective in their intended therapeutic role, performance gains in healthy populations are modest at best and profoundly inconsistent across subgroups and individuals. We propose a method for  The concept of cognitive enhancement is age-old, but despite modern advances in our understanding of the cellular and molecular bases of cognition, much remains unknown. Development of new cognitive enhancers with increased specificity, safety, and effectiveness must outpace increasing demand, particularly as off-label and illicit use of current drugs has become common in certain population subgroups Maher,  and as tCognition is complex and involves multiple domains, from learning and memory to attention, and cognitive enhancers can target one or more. At present, two of the most widely cited cognitive enhancers are the dopaminergic stimulants methylphenidate, prescribed for treatment of ADHD, and modafinil, prescribed for treatment of sleep disorders, such as narcolepsy. While both of these drugs can enhance memory and attention in healthy individuals  to those still under investigation: ampakines, nicotinic receptor agonists, glutamate receptor agonists, glycine inhibitors, and PDE inhibitors  a better understanding of the neurobiological basis of cognition; and (b) drugs with higher specificity, greater effectiveness, and fewer side effects. To this end we propose an expedited path to understanding the molecular basis of cognition and to subsequent drug discovery via a combination of advanced gene expression analysis, signaling pathway analysis, and Enhancing cognition is a challenge as it consists of various processes each influenced by many factors and each with a distinct neurobiological framework. While single-gene studies have been an effective first step in identifying individual elements necessary for learning and memory to occur , owing to the complexity of protein interactions within signaling pathways and lack of experimental data to determine the importance factor for each member of a given pathway. This problem was addressed, however, with the development of Oncofinderin silico screening to predict and rate drugs that target these pathways, depending on the application; and (5) in vitro and in vivo validation.PAS analysis involves (1) mapping relevant pathways from the gene expression profiles for a given condition; (2) calculating individual PAS values; (3) constructing the signaling pathway cloud (net activated and repressed pathways); (4) high throughput The pair of conditions to be compared in signaling pathway cloud analysis is flexible. In the past, we have compared tumor biopsies to healthy tissue, revealing signaling pathway biomarkers that outperform those of single genes  and finally by calculating disturbance of the associated signaling pathway cloud Figure .in vivo or in vitro.High throughput screening for drugs that activate or repress key pathways could then uncover drugs that replicate cloud disturbance in “enhanced” models and thus mimic or exaggerate the effects of the current drugs or genetic manipulations. These drugs could then be ranked and prioritized for validation of cognitive effects These methods can be used for general screening of cognitive enhancers at present but in the future may also be used to develop personalized cognitive enhancement plans for individual patients.The idea of using differential gene expression patterns for drug discovery is not new. The broad variability of as well as the error rates introduced by the microarray and next generation sequencing (NGS) equipment led to many early failures, impeded progress and diminished the potential of this approach. However, recent algorithmic solutions reducing complexity and smoothing variability of the gene expression mapped onto signaling pathways allow us to minimize errors and perform cross-platform analysis  value, one can simply switch from using absolute values of expression levels to their logarithms, arriving at the PAS value for each pathwayHere, the multiplication is performed over all possible activator and repressor proteins in the pathway, and [enhanced-to-control ratio (ECR), one divides the expression levels for a gene n in the sample taken for the enhanced group by the same average value for the normalized control group. The discrete value of activator/repressor role (ARR) is the relative strength of the target activation or repression.In the case of cognitive enhancement, to obtain the values of BTIF  equals to zero when the ECR value lies within the tolerance limit, and to one when otherwise. We determined that the ECR lies beyond the tolerance limit if it satisfies simultaneously the two criteria. First, it should be either higher than 3/2 or lower than 2/3 of the corresponding gene expression level in normal group of samples, and, second, the expression level of a gene should differ by more than two standard deviations from the average expression level for the same gene in a control group of samples.The information about the activator/repressor role of a particular gene product/protein may be obtained from the analysis of open-access or customized pathway databases and from the literature. The Boolean flag of Acquiring gene expression data from the various regions of human brain during excitation is difficult and human data would have to be obtained from post mortem tissues or cultured cells or tissues. Gene expression data from mouse brain is easier to obtain, but further studies are required to evaluate the correlations between signaling pathway activation profiles in mice and humans. Tissue selection is also important, gene expression in one region or cell type may or may not be representative of other areas of the brain and may not capture all effects of a given condition. Also, gene expression alone may not provide the complete picture of the state of the tissue, and while the pathway activation analysis approach may be used to analyze differences in genomic DNA (Spirin et al., in silico screening and ranking of drugs and other factors that act on signaling pathways involved in cognition. Predicting their efficacy would involve calculating their potential to maximize the difference in signaling pathway activation between cells or tissues of cognitively enhanced animal models, including mutant “smart” vs. wild type mice, high-performing vs. low-performing strains of mice, or drug-treated vs. drug naïve mice. This would be followed by in vitro and in vivo validation leading to a short list of promising components.Cognitive enhancement is in demand, whether in healthy populations or those with cognitive deficits, but current pharmacological enhancers offer only modest benefits. Testing of new cognitive enhancers is costly and time consuming. Even predicting the nootropic candidates out of the hundreds of thousands of drugs and drug combinations remains a major challenge. A screening process that would predict the efficacy of novel cognitive enhancers that may outperform current options would save time and money, both of which are limiting as the aging segment of the population explodes over the upcoming decades, with increasing rates of cognitive decline and neurodegenerative disease. Here we propose a method for Alex Zhavoronkov, Alex Aliper and Anton Buzdin are affiliated with the commercial companies searching for cognitive modulators and nootropics. The intent of this paper is to provide insight into the methods explored by these companies and shortlist the pathways implicated in cognitive performance.


Beet curly top virus (BCTV). The C4 protein of BCTV is sufficient to induce hyperplasia and alter Arabidopsis development. It was previously shown that C4 interacts with two Arabidopsis Shaggy-like protein kinases, AtSK21 and 23, which are negative regulators of brassinosteroid (BR) hormone signaling. Here we show that the C4 protein interacts with five additional AtSK family members. Bikinin, a competitive inhibitor of the seven AtSK family members that interact with C4, induced hyperplasia similar to that induced by the C4 protein. The Ser49 residue of C4 was found to be critical for C4 function, since: 1) mutagenesis of Ser49 to Ala abolished the C4-induced phenotype, abolished C4/AtSK interactions, and resulted in a mutant protein that failed to induce changes in the BR signaling pathway; 2) Ser49 is phosphorylated in planta; and 3) plant-encoded AtSKs must be catalytically active to interact with C4. A C4 N-myristoylation site mutant that does not localize to the plasma membrane and does not induce a phenotype, retained the ability to bind AtSKs. Taken together, these results suggest that plasma membrane associated C4 interacts with and co-opts multiple AtSKs to promote its own phosphorylation and activation to subsequently compromise cell cycle control.Even though plant cells are highly plastic, plants only develop hyperplasia under very specific abiotic and biotic stresses, such as when exposed to pathogens like  Geminiviridae) cause extensive agricultural losses in crops worldwide ) C4 protein and the Tomato golden mosaic virus (TGMV-[BR:Com:84]) AC4 protein interacted with two members of the Arabidopsis SHAGGY-like protein kinase family (AtSK21 and AtSK23), which are involved in the brassinosteroid (BR)-signaling pathway ) was shown to interact with a novel SHAGGY-like protein kinase (SlSK) of tomato that is closely related to AtSK21 and AtSK23 ) infection ), Beet severe curly top virus BSCTV-[US:Cfh], Spinach curly top virus (SpCTV-[US:Sp3:36]), and two monopartite begomoviruses, Tomato yellow leaf curl virus (TYLCV-IL[ES:Alm:Pep:99]) and ToLCV-To (N. benthamiana plants following inoculation with genomic transcripts from a potato virus X based vector (pPVX) [C4 or the C4-mutants. A non-translatable C4 (C4nt) served as a negative control [To identify amino acids critical for C4 function, alanine-scanning mutagenesis  was perfToLCV-To . The C4 r (pPVX)  carryingin vitro at undefined serine and threonine residues [N. benthamiana plants, whereas the Thr18 (C4A18T) phenotype remained the same  or C4A49T (IPC4A49T-2) at levels similar to wild-type C4 in transgenic line IPC4-28 (see below) were chosen for further study. IPC4S49A-2 and IPC4A49T-2 phenotypes, with and without ß-estradiol induction, were compared to transgenic line IPC4-28. Seedlings of the three lines developed normally and were indistinguishable from wild-type Arabidopsis when germinated in the absence of the inducer  mutants of the sg2AtSKs. The catalytically inactive AtSK21K69R mutant (referred to hereafter as AtSK21-KD) was unable to phosphorylate BZR1 ,48. We gn planta . The expn planta . Taken tTo determine the biological significance of the loss of C4 function, we assayed the effect of C4 expression on BR signaling. Expression of C4 resulted in a shift between phosphorylated and hypophosphorylated BES1 levels, and a positive correlation between the steady state levels of C4 protein and hypophosphorylated BES1 . We hypoCPD and DWF4, are repressed in the presence of the C4 protein [CPD and DFW4 transcript steady state levels. In contrast, the reversion mutant, C4A49T, should mimic C4. CPD and DWF4 transcript levels were compared in induced IPC4-28, IPC4S49A-2, IPC4A49T-2 and IPC4nt-12 seedlings relative to non-induced seedlings by qRT-PCR at 24 h post-induction  in the presence of wild-type C4, while no significant changes in the expression levels of CPD and DWF4 (<1.5-fold) were detected in the presence of C4S49A  in the presence of the revertant, C4A49T. These results corroborate the conclusion from To further confirm the role of C4 in altering BR-signaling, we used qRT-PCR analysis to determine the expression levels of BR-signaling pathway target genes in seedlings expressing C4S49A. We previously showed that BZR1 target genes, nduction . Steady f C4S49A . As expeN. benthamiana . While the importance of a phosphoacceptor residue at non-conserved residue 21 was not further pursued in this report, phosphorylation of a phosphoacceptor residue at position 49 was found to be required for C4-induced symptoms. Studies on recombinant BCTV C4 and TGMV AC4 proteins previously identified Ser49 as one of four amino acids in the central region of the protein that play a role in the C4 phenotype, although the significance of each individual amino acid was not determined . In C4, function . Future in planta correlates with the loss of phenotype following induction of C4S49A in transgenic plants, the loss of the C4-induced phenotype when C4S49A was expressed from a chimeric PVX-vector following inoculation onto N. benthamiana plants, the inability of C4S49A to affect the phosphorylation status of phosphorylated BES1 and the inability to modify BZR1 target gene (CPD and DWF4) transcript steady state levels. Second, C4 and C4A49T interactions with sg2AtSKs localize to the plasma membrane and the nucleus, while the mutant C4G2A interacts strongly with AtSK23 in the cytosol and nucleus in planta, but does not localize to the plasma membrane and fails to induce a phenotype. Therefore, only C4/AtSK interactions at the plasma membrane are functionally important.Mutants of C4 that disrupt C4/AtSK interactions (C4S49A) or localization of C4 to the plasma membrane (C4G2A) fail to induce hyperplasia or alter seedling development. The biological importance of the C4/AtSK interactions at the plasma membrane was supported by the following findings. First, the lack of a detectable interaction between plasma membrane localized C4S49A and AtSK23 Arabidopsis. In contrast, BL, which negatively regulates the BR response by inhibiting sg2AtSK and AtSK32 activities [Our results show that bikinin, which specifically inhibits sg1AtSKs, sg2AtSKs and AtSK32 , inducestivities ,24,25, htivities .50 of 23.3 μM for bikinin in a hypocotyl elongation assay (56).The observed C4/AtSK interactions suggest that the C4 protein, similar to bikinin, binds directly to the same seven AtSKs, which could explain the phenocopying induced by C4 and bikinin. Bikinin induces hyperplasia in a concentration dependent manner with significant hyperplasia being observed at ≥30 μM bikinin. At bikinin concentrations of ≥50 μM, bikinin-induced hyperplasia was indistinguishable from C4-induced hyperplasia. Previously, bikinin (30 μM) was shown to induce significant developmental changes by 3 days post treatment  and to iThere is considerable complexity in the role of AtSKs in a variety of biological processes . HoweverThe C4 protein provides an invaluable tool for expanding our understanding of the host factors and regulatory pathways involved in cell cycle regulation and to provide insights into the relationship between the plant cell cycle and development. In this study, we provide evidence that C4 function is activated by phosphorylation of Ser49 by members of the AtSK family and suggest that the protein regulates multiple AtSK activities that may subsequently disrupt mitotic control. Our results are consistent with a model in which C4 functions by sequestering multiple members of the AtSK family to the plasma membrane or by competing with AtSK target proteins resulting in inhibition of an array of AtSK functions. The possible disruption of AtSK function(s) by C4, which might equate to a similar response by bikinin, results in hyperplasia. Alternatively, an intriguing possibility is that C4 has an as yet undefined function after being activated by phosphorylation, which may modulate one or more signal transduction pathways at the plasma membrane, that result in hyperplasia. Future studies aimed at defining the specific C4/AtSKs interactions that result in hyperplasia and the role of phosphorylation in C4 function will reveal additional insights into the mechanism by which the C4 protein regulates cell division.S1 FigN. benthamiana. For comparison, phenotypes of transgenic plants expressing the C4nt negative control and the C4 positive control. Third systemic leaf collected from plants expressing C4nt, C4, C4-cEYFP, or C4-nEYFP shown in the bottom panel. B. Confocal micrographs of C4 fused to EYFP agroinfiltrated into N. benthamiana CFP-H2B marker plants. C. Confocal micrographs of AtSK22, AtSK23, and AtSK41 fused to mCherry agro-infiltrated into N. benthamiana CFP-H2B plants. AtSK22-, AtSK23- and AtSK41-mCherry fusion proteins localize to the cytoplasm, evidenced by cytoplasmic strands (arrow) and cytoplasmic granules (arrowhead), and plasma membrane. The mCherry signal also co-localized to the nucleus with the CFP reference nuclear signal (CFP and overlay). Arrowhead, cytoplasmic granule; arrow, cytoplasmic strands. Scale bar = 50 μM.A. Phenotypes induced following inoculation of transcripts from pPVX-C4-nEYFP  or pPVX-C4-cEYFP  onto (TIF)Click here for additional data file.S2 FigNon-specific protein used as a loading control (LC) is shown below each immunoblot. Mw: AtSK21 = 43.01 kDa, AtSK23 = 46.53 kDa, AtSK41 = 47.68 kDa.(TIF)Click here for additional data file.S3 FigN. benthamiana plants. Transcripts from pPVX-C4 were used as a positive control. Transcripts from pPVX-C4nt and pPVX were used as negative controls. Plants shown at 14 days post inoculation. pPVX, potato virus X expression vector.Phenotypes induced following inoculation of transcripts from pPVX-C4 mutants onto (TIF)Click here for additional data file.S4 FigA. IPC4SII transgenic seedlings grown on solid media (Non-Induced) or solid induction media (Induced) at 12 days post-induction. Scale bars = 4 mm (Non-induced) and 2 mm (Induced). B. Immunoblot of extracts from induced IPC4-28, IPC4SII-6 and IPC4A49TSII-1 seedlings prior to affinity chromatography (Pre-affinity column) or following affinity chromatography (Post-affinity column). Non-specific protein used as a loading control (LC) is shown in the lower panel of Pre-affinity Column samples. Non-specific proteins were not present in Post-affinity Column samples. Mw: C4 = 9.69 kDa, C4SII = 10.89 kDa, C4A49TSII = 10.90 kDa.(TIF)Click here for additional data file.S5 Fig21DYSTWYPQPGQHISIR, ELNPAPTSpS49PTSTR, and ELNPAPTSpT49PTSTR, respectively. Their precursor ions are shown at the top of each spectrum. The key fragment ions are emphasized at the sequence diagrams for ambiguous phosphorylation site assignments.A-C are the Collisional Induced Dissociation MS/MS spectra of SQIpS(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


As a web server, PathWhiz is accessible from almost any place and compatible with essentially any operating system. It also houses a public library of pathways and pathway components that can be easily viewed and expanded upon by its users. PathWhiz allows users to readily generate biologically complex pathways by using a specially designed drawing palette to quickly render metabolites (including automated structure generation), proteins , nucleic acids, membranes, subcellular structures, cells, tissues and organs. Both small-molecule and protein/gene pathways can be constructed by combining multiple pathway processes such as reactions, interactions, binding events and transport activities. PathWhiz's pathway replication and propagation functions allow for existing pathways to be used to create new pathways or for existing pathways to be automatically propagated across species. PathWhiz pathways can be saved in BioPAX, SBGN-ML and SBML data exchange formats, as well as PNG, PWML, HTML image map or SVG images that can be viewed offline or explored using PathWhiz's interactive viewer. PathWhiz has been used to generate over 700 pathway diagrams for a number of popular databases including HMDB, DrugBank and SMPDB.PathWhiz ( Pathway diagrams are the road maps of biology. They have long been used as visual tools to map out complicated biological processes over space and time. In their simplest form, pathway diagrams can be used to illustrate the connections between genes, proteins and/or metabolites at the cellular or subcellular level. More complicated pathway diagrams attempt to extend these connections to tissues, organs or entire organisms, thereby adding even greater biological context. Regardless of whether the actual pathways are simple or complicated, the goal of a pathway illustration is to render complex biological processes and connections in a way that a viewer can easily understand. There is no single correct way to illustrate a biological pathway. Indeed, the level of detail and the amount of information conveyed for identical biological pathways can vary tremendously.These differences often depend on the intended purpose or the state of knowledge of a given pathway. Certainly when pathway diagrams are created for textbooks or wall charts, the pathway illustrator is expected to generate complex, richly detailed, colourful works of art . In thesMore recently, a number of these pathway databases have made their software available to allow others to illustrate or generate web-compatible pathway diagrams. In particular, BioCyc's Pathway Tools , WikipatIdeally what is needed is a freely available, user-friendly, platform-independent system (i.e. a web server) that provides the capacity to generate colourful, visually pleasing and biologically complete pathway diagrams like those found in textbooks while at the same time supporting the generation of machine-readable, interactive, richly hyperlinked pathway diagrams that are compatible with modern web browsers. The fact that no such software system exists led us to develop a novel concept for pathway illustration: a pathway-drawing web server called PathWhiz.PathWhiz is essentially a web server designed for the facile creation of colourful, visually pleasing and biologically accurate pathway diagrams that are machine-readable, interactive and fully web compatible. PathWhiz differs from other pathway drawing tools in that it is a web server rather than a stand-alone program. Therefore, unlike most downloadable software programs, PathWhiz is platform independent. This makes PathWhiz accessible from almost any place and compatible with essentially any operating system. In addition, its web presence allows PathWhiz to house a public library of pathways and pathway components that can be easily viewed, used, manipulated and constantly expanded upon by its users. PathWhiz also differs from most other pathway drawing tools with regard to the level of biological detail, physiological context and biological complexity that it can support. This is because it uses a specially designed drawing palette to render metabolites (including automated structure generation), proteins , nucleic acids, membranes, subcellular structures, cells, tissues and organs. Both small-molecule and protein/gene pathways can be constructed using a simple graphical interface to combine specific pathway processes such as reactions, interactions, binding events and transport actions. Pathways drawn in PathWhiz can be saved in machine-readable BioPAX, SBGN-ML  and SBML (Systems Biology Markup Language) –17 formahttp://rubyonrails.org, version 4.2.0) web framework incorporating a MySQL relational database  to manage all of the pathway data, including entity relationships, external references, descriptions, visualization specifications and chemical structures. The front-end web client is controlled by Ruby on Rails combined with Backbone.js  as the front-end web framework for the editor. PathWhiz collects and manages all the data necessary to produce PNG and SVG images, as well as BioPAX , while custom Java plugins have been developed for BioPAX, SBGN-ML and SBML generation using the Paxtools  libraries, respectively. PathWhiz has been tested and found to be compatible with most modern web browsers including Google Chrome (v. 31 and above), Internet Explorer (v. 9 and above), Safari (v. 7 and above), Opera (v. 15 and above) and Firefox (v. 23 and above).PathWhiz consists of three major components: the Pathway Editor, the Pathway Viewer and the PathWhiz Data Repository for capturing metadata about each pathway and pathway object or process. Pathways are generated and edited using the Pathway Editor while the Pathway Viewer is for visualizing, printing and downloading the finished pathways. PathWhiz was built on a Ruby on Rails —guest users are anonymous, and anyone can add a pathway or edit an existing pathway. To prevent guest pathway diagrams from being corrupted or vandalized, pathways have a ‘lock’ functionality that allows guest users to lock (or prevent) future editing of pathways by other guest users. Once a pathway is locked, it cannot be unlocked.PathWhiz can be accessed either as a guest user or by creating a ‘private’ user account. The functionality of the two account types is essentially identical, with both guest users and ‘private’ users having access to the exact same editing capabilities and pathway or pathway component libraries. The main difference is that the content created by a guest user can be edited by all other guest users, while the content created by a ‘private’ user can only be edited by the account holder. The guest model for PathWhiz is akin to that of Wikipedia (A locked pathway can still be selected by another user for replication (pathway replication is described in further detail later), which allows any guest user to duplicate that pathway, edit it and to save it as either a locked or unlocked pathway under a different title. This replication function allows existing pathways to be edited or altered by anyone without damaging the original pathway. Unlike guest-drawn pathways, pathway diagrams created by a user with his/her own account can only be edited by that account holder. These pathways can still be viewed and replicated by others if the user makes them publically available (which is strongly encouraged), however, they also can be made private, meaning no other users will be able to view them. This private user account option on PathWhiz allows individuals to create and save pathways that they may not wish to have public (due to publication issues or privacy concerns), to create private collections of pathways for their own use or to satisfy the need of some users to have appropriate attribution if they choose to make their personal pathway diagrams public. The developers of PathWhiz respect user privacy. Private pathway data will not be altered by anyone, including the PathWhiz web server administrators, without user permission, for as long as the web server is operational and as long as the user indicates the pathway data should remain private.In PathWhiz, pathways are essentially stored and represented as two parts: a pathway model and a pathway visualization. Pathway models use two standard components: biological elements and biological processes, each of which is described by a specific set of metadata. Element types consist of metabolites, proteins/enzymes, cofactors, nucleic acids and biological states, while processes consist of reactions, interactions, binding events and transport activities that reference the aforementioned elements. The element and process metadata includes information such as names, accession numbers, structures, sequences, external references, cardinality, directionality, biological locations and other kinds of notes or annotations. Both elements and processes can be entered into and accessed from the public PathWhiz database using the Pathway Editor Figure  and b. Dhttp://smpdb.ca/pathwhiz/legend). If the appropriate metadata is available in the database, PathWhiz can also automatically render chemical structures for small-molecule compounds, choose appropriate subunit structure templates for proteins and naively arrange the elements in protein complexes.Once entered into the database, pathway elements and pathway processes can be added to a pathway model and rendered in the Pathway Editor's visualization module. This pathway drawing system is a specially designed, online graphical workspace where pathway components can be rendered and manipulated to create the desired pathway visualization Figure  and d. Phttp://smpdb.ca/pathwhiz/guides). Once a pathway diagram is completed it can be exported to various graphical and data exchange formats, as well as viewed in the interactive Pathway Viewer, which is described later.The PathWhiz drawing system also contains a membrane tool that allows the free-form rendering of simple or complex membranes colour-coded to indicate different membrane types. Pathways can be further augmented with the drawing system's library of stock images, which correspond to other kinds of biological elements such as organs, cell organelles, or biological ‘states’ such as mutated proteins. There are also non-biological visualization components that can be utilized to aid pathway depiction, including customizable labels, lines, and zoom boxes. In effect, the large variety of visualization options available in the drawing system's library allows the generation of pathway representations from very simple, black and white wire depictions to very detailed, colourful and image-rich diagrams Figure . Quick sThe Pathway Editor also contains pathway replication and pathway propagation functionality. The replication function essentially allows users to take any existing public pathway (including those that are locked or those that belong to ‘private’ users) and duplicate it so that it may be altered and/or combined with other visualizations and saved as a new pathway. Pathway replication can be done both at the start of pathway creation (i.e. a new pathway can be generated using an existing pathway as a starting template) as well as at any point during the pathway drawing process (i.e. an existing pathway can be copied/imported into the current pathway). Replication allows users to alter and append existing pathways without having to redraw complex pathways that have already been depicted by other users.Pathway propagation is an extension of pathway replication and was developed to allow pathways to be replicated and mapped to other species (where protein names or accession numbers are different). Pathway propagation is performed using the same mechanisms as pathway replication. If the species of the new pathway is not the same as the species of the original pathway, PathWhiz automatically converts any proteins in the original pathway to the proteins corresponding to the new species. This is done using the Basic Local Alignment Search Tool (BLAST)  on UniPrhttps://www.google.ca/maps). This is done using standard navigation buttons in addition to conventional mouse-activated click, drag and scroll operations. We believe this type of image navigation is very appropriate for viewing biological pathways since they are essentially road maps for biologists. The side bar of the PathWhiz Pathway Viewer provides a highlighting ability, allowing users to quickly find and jump to specific entities of interest. The side bar also provides simple analysis functionality, where users may annotate and visualize experimental metabolomic or proteomic data by mapping metabolite/protein concentrations to a colour gradient that is applied to the annotated elements. Also found on the side bar are visualization options (membrane complexity and background colour) as well as download links to the different data formats, which are described below. If the appropriate metadata is available, pathway elements in the Pathway Viewer are hyperlinked and when clicked will provide the user with a pop-up window that gives a synoptic view of the selected element, including its participating processes, biological state information and external database links.PathWhiz's Pathway Viewer Figure  providesThrough the Pathway Viewer, users may download pathway diagrams in a variety of formats for further offline use. In particular, pathway diagrams can be downloaded as SVG and PNG images, as well as in BioPAX  SBGN-ML As BioPAX and SBML are (currently) only able to store pathway model data and not visualization information, these formats hold detailed metadata for pathway elements and processes but lose their layout information in the conversion. Conversely, as SBGN-ML is the markup language for SBGN, a standardized graphical notation , it retaPathway diagrams drawn by skilled artists are typically the most aesthetically pleasing and easily understood of all forms of pathway visualizations. Hand-drawn pathways give the illustrator/scientist tremendous freedom to represent a pathway in a way that best communicates their understanding of the biology and the biological process being depicted. However, if one is interested in drawing dozens or hundreds of pathways or if one wants pathway illustrations to be compatible with the internet  hand drawn pathways can be problematic. In particular, relying on a single artist or giving one or more pathway illustrators complete artistic freedom can lead to significant problems with productivity, internet compatibility, quality, consistency and interpretability. These problems exist because drawing pathways is just as much an art form as it is scientific pursuit, and each artist has his or her own style; that style can change or develop over time and even vary depending on the subject. Certainly many problems with consistency can be mitigated by the use of standard operating procedures (SOPs), but even SOPs will not eliminate the need for external reviewers and editors to ensure that pathways meet quality specifications.These problems are not unique to hand-drawn pathway databases. Issues of quality, consistency and completeness seem to plague even the best internet pathway databases, even with their simplified wire-frame schematic diagrams. Nevertheless, these were precisely the problems we experienced when we first began development of a pathway database called SMPDB in 2009 . The goaIt was from the many frustrating experiences associated with constructing and maintaining the first version of SMPDB that we decided to develop PathWhiz. Essentially, PathWhiz is an attempt to combine the advantages of software-rendered pathway drawings with the aesthetic appeal of higher quality hand-drawn pathway diagrams. In effect, PathWhiz incorporates hard-coded SOPs into a pathway drawing program, thereby creating an environment for the generation of consistent and aesthetically appealing pathway drawings supported by rigorous data collection standards. While not yet perfect, we believe PathWhiz addresses many of the problems and shortcomings of our previous pathway drawing efforts. It also offers a number of important features and has some significant advantages over other pathway drawing tools. First, PathWhiz supports the generation of colourful, complex, visually pleasing and biologically rich pathway diagrams in a highly standardized way. Second, it allows for the simple and rapid generation of image-mapped pathway diagrams that can be easily zoomed and navigated. Third, it provides a framework for reading, writing and rendering pathway diagrams in machine-readable, easily exchanged formats . Fourth, as a web server, PathWhiz makes biological pathway generation far easier and pathway accessibility much greater because it is largely platform-independent and accessible from almost anywhere, anytime. Fifth, because of its web server design, PathWhiz supports community pathway contributions. This opens the door to a form of pathway crowd sourcing to more rapidly generate new pathways for database deposition and sharing.Many different users have extensively tested PathWhiz during its development over the last few years. Throughout this testing period its operations have been optimized and its GUI streamlined through constant user feedback. As a result of these improvements, PathWhiz was recently used to generate more than 700 different human metabolomic pathways for version 2.0 of SMPDB , coverinhttp://smpdb.ca/pathwhiz/downloads). However, it is important to note that the elements in PathWhiz were  added by users in response to user needs, thus the PathWhiz repository is not meant to serve as a primary resource for biological data. Instead, the PathWhiz repository is more of a pathway art studio rather than a pathway art gallery or database. An art studio typically has finished works or art, unfinished works of art as well as art ‘failures’, while a gallery only presents the best or most complete works of art.Since it's inception 2 years ago, the PathWhiz studio has grown from 124 pathways in August 2013 to 722 pathways in August 2014, and currently contains a total of 819 pathways . It also houses 40,947 small-molecule compounds  and 9625 proteins . All of PathWhiz's data is available for download on the ‘Downloads’ page (Overall, we believe that the graphical tools and visualization capabilities of today's modern web browsers have moved well beyond the limitations associated with simplified wire-framed or schematic pathway diagrams. Indeed, many of these older pathway drawing tools and their associated pathway diagrams or databases were developed in the very earliest days of the internet. Given that more than a decade has passed since these early tools and earlier drawing conventions appeared, we believe the time is ripe for a change. A new generation of pathway drawing tools, a new generation of pathway databases and a new approach to generating, sharing and visualizing biological pathways on the internet need to be considered. By releasing PathWhiz as a public web server, we are hoping that it will inspire others to contribute additional pathways to the scientific community and to further enhance both PathWhiz's capabilities and the quality of biological pathways found on the internet.


Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities. Psoriasis is a chronic immune mediated skin disease. The disease presents itself with well circumscribed, red or silvery scaly plaques .The pathThe occurrence of one or more disorders in association with a particular disease has recently gained interest in various medicinal divisions including dermatology –5. MultiThe current work aims to decipher the shared component hypothesis between psoriasis and its five associated common comorbidities which includes myocardial infarction (MI), type II diabetes (T2DM), obesity, rheumatoid arthritis (RA) and Alzheimer’s disease (AD). The psoriasis associated comorbidities would be associated at the molecular level by common genes, proteins, biological processes and pathways. The network medicine approach has been utilized to construct individual diseasomes. The interactomes are explored to identify the biological processes and pathways linking psoriasis with its comorbidities.Table 1.Gene expression raw data (CEL files) were downloaded from NCBI GEO database . A singlThe microarray expression sets were processed individually in a similar way to remove bias. The raw files from different studies were processed using the bioconductor packages in R –15. The The association between psoriasis and its comorbidities was termed as “diseasome”. The two diseases were linked if they share the variations in similar set of genes. Proteins encoded by each differentially expressed gene were identified for the construction of the diseasome. The human proteome interactome was obtained based on the interactions reported by the HPRD server .The streDis1 Proteins and ProteinDis2 Proteinswere the proteins associated with disease1 and disease2 respectively. ProteinsDis1→Dis2 Proteinswere the proteins associated with disease1 that show interactions with the proteins associated with disease2 (vice versa for ProteinsDis2→Dis1 Proteins). ∩ was the intersection operator denoting the number of common proteins between the diseases and ∪ operatordenote the total number of proteins participating in both the disease categories. The sets represented within the vertical bars indicate their cardinality.ProteinsTo explore the significance of biological functions of the differentially expressed proteins in each disease  a functiThe Jaccard coefficient measures the degree of similarity between the psoriasis comorbidities. Dis1 and Dis2 correspond to psoriasis and its comorbidities. Biological process (BP) of Dis1 and Dis2 represents the biological processes contributed by psoriasis and its individual comorbidity respectively, whereas pathways of Dis1 and Dis2 are the biological pathways in which the proteins associated with the comorbidities participate. The calculated measures were visualized as heat-maps constructed using gitools .Table 2). Each disease category had a minimum of one disease and a maximum of four disease classes. Five diseases based on their common co-occurrence with psoriasis were selected to study their relationship with psoriasis. The comorbidities associated with psoriasis chosen for the study were T2DM, obesity, MI, AD and RA. The association between psoriasis and the five major comorbidities were verified against the published literatures [The differential expression analysis of psoriasis microarray data resulted in 507 differentially expressed genes (DEG) , on submitting the differentially expressed genes of psoriasis transcriptome to DAVID server, around fourteen major disease categories were enriched (eratures –25.S1 Table). We constructed a psoriasis specific interactome and utilized the knowledge obtained from the interactome to mine out the commonality between psoriasis and its comorbidities . The psoriasis diseasome constructed using the knowledge obtained above revealed that the comorbidities were connected with psoriasis through shared genes or proteins which interact to form the cellular interactome.Independent meta-analysis was carried out for psoriasis and its co-morbidity using their respective microarray expression data. The aim of the meta-analysis was to identify the genes which were significantly upregulated and down regulated in diseased condition when compared with the normal samples –27. The Figs 5). AD and psoriasis were directly linked through 13 proteins, while the association also occur indirectly through the interaction of 84 proteins. In case of T2DM 26 proteins directly connected both the diseases and 210 proteins contribute to their interaction via partnering with other proteins. MI and psoriasis were connected directly through 7 proteins and indirectly through 49 proteins. Similarly obesity and psoriasis were connected directly through 24 proteins and indirectly through 11 proteins. RA showed highest percentage of connections. They were connected directly through 201 proteins and indirectly through 386 proteins . The Molecular Comorbidity Index (MCI) which shows the strength of association between psoriasis and its comorbidities was also studied for the better understanding. T2DM leaded the MCI list followed by RA, AD, MI and obesity . To assess the role of direct and indirect contributors to the diseases we further studied the differential expression patterns of the linker proteins.The commonalities between the diseases were assessed based on their interaction patterns. The number of shared genes or proteins in the psoriasis diseasome ranged from 31 (MI) to 312 (T2DM) . The DEGs of the comorbidities were compared with DEGs of psoriasis individually. We noted significant overlaps between the DEGs upregulated in psoriasis and those downregulated in its comorbidities. Similarly DEGs downregulated in psoriasis were observed to be overlapping with the DEGs upregulated with psoriasis associated comorbidities. Considerable overlap was also observed between DEGs deregulated in same direction between psoriasis and its comorbidities. The DEGs deregulated in the same direction can be treated as putative signatures of the comorbidities while the DEGs deregulated in the opposite direction can contribute to inverse comorbidities. We noted a maximal hit of DEGs deregulated in both the direction was from T2DM followed by RA, obesity, AD and MI. Highest number of genes upregulated in the similar direction was reported in T2DM followed by RA, AD, obesity and MI. Similarly the highest number of down-regulated genes in the same direction was seen in RA followed by obesity, AD, T2DM and MI. The highest number of DEG upregulated in opposite direction followed the same pattern of DEGs upregulated in similar direction while the highest number of DEGs down-regulated in opposite direction was observed in AD followed by obesity, T2DM, MI and RA. When the overall DEG count was compared the genes expressed in similar directions were high contributing to the positive comorbidity.The deregulation of common set of genes either in similar or opposite direction can be an underlying cause for the comorbidities. To generate the molecular interpretation of the comorbidities, we compared the direction of dysregulation of the genes shared by psoriasis and its comorbidities with the help of meta-analysis (S2 Table). Defence response, immune response and inflammatory response were enriched in all the comorbidities. Response to wounding showed a similar pattern of expression in almost all the comorbidities except T2DM. Proteins contributing to cell-cell signaling were upregulated in AD, T2DM and RA. Around five biological processes were shared by obesity and RA with connection to psoriasis. To investigate the degree of similarity between psoriasis and its comorbidities, Jaccard coefficient was computed for all the comorbidities . The highest JC value attributed to obesity followed by T2DM, RA, MI and AD. These observations highlights the critically impaired BP between psoriasis and it comorbidities were from immune mediated processes.To identify the biological functions shared by the psoriasis and its comorbidities, a functional enrichment analysis was performed based on the BP. RA shared the highest number of overlapping BP with psoriasis. The list followed the order of T2DM, obesity, AD and MI starting from highest to lowest overlapping processes (S3 Table). Highest number of pathway overlap was observed with RA followed by T2DM, obesity, AD and MI. Similar to the BP, the degree of similarity in the pathways between psoriasis and its comorbidities was also assessed using Jaccard coefficient . Psoriasis shared the highest number of pathways with obesity followed by RA, MI, T2DM and AD. We noted eight pathways were overlapping with all the comorbidities. Angiogenesis, CCKR signaling map, Gonadotrophin-realising hormone receptor pathway, heterotrimeric G-protein signaling pathway-Gi alpha and Gs alpha mediated pathway, heterotrimeric G-protein signaling pathway-Gq alpha and Go alpha mediated pathway, inflammation mediated by chemokines and cytokine signaling pathway, integrin signaling pathway and Wnt signaling pathway. Toll like receptor signaling pathway was shared by AD, T2DM, MI and RA. Seven pathways involved in blood coagulation, Cadherin signaling, FGF signaling, Ionotropic glutamate receptor pathway, Metabotropic glutamate receptor group III pathway, Muscarinic acetylcholine receptor 1 and 3 signaling pathway, and Nicotinic acetylcholine receptor signaling pathway were enriched in AD,T2DM, obesity and RA. PDGF signaling pathway was found to be overlapping with T2DM, MI, obesity and RA. It was observed that the biological pathways involved in the comorbid diseases were not only shared by psoriasis but also by the other diseases which are almost unrelated to each other except few. These observations shed lights over the existing associations between the psoriasis with its multi-morbidities at the pathway level.We further investigated the pathways that were common between the psoriasis and its associated comorbidities  in psoriasis and AD. Lipid metabolism is the common term connecting both the disease. ApoE polymorphisms were shown to be associated with both the diseases disrupting the lipid metabolism. When the direction of regulation was considered the gene was downregulated in both the cases as established by the previous reports –30. An eThe findings of our work reveal that the psoriasis comorbidities are related at the molecular level that may contribute to their co-occurrences. In this context, the BP annotation identified several process of inflammation such as defence response and immune response which were enriched in all the comorbidities. The pathway annotation also revealed the presence of inflammatory pathways such as inflammation mediated by chemokines and cytokine signaling pathway, integrin signaling pathway and Wnt signaling pathway. The search for inflammatory links was carried out for all the diseases against published literatures.The production of amyloid-β peptides can activate the innate immune response and evoke AD. Pattern recognition receptor (PRR) such as C1q is involved in complement cascade activation in the brain. The PPRs were also involved in the induction of pro-inflammatory signaling pathways in AD . VariousPsoriasis and its associated comorbidities highlight psoriasis as a paradigmatic disorder. The comprehensive network based approach investigated the shared component hypothesis existing between psoriasis with its associated co-morbidities. The findings suggest that the most prevalent psoriasis comorbidities were interlinked through common molecular connections which were evidenced by their shared biological functions and pathways. Significant overlap was observed in the biological process and pathways involved in inflammation in most of the comorbidities establishing a link between them. The work identified few associations in the biological processes and molecular pathways which were previously overlooked in majority of the diseases. The current research also shed light on few multimeric novel targets and pathways which can be targeted to offer diagnosis and/or cure for psoriasis along with its associated co-morbidities.S1 Table(DOCX)Click here for additional data file.S2 TableThe common biological processes between psoriasis and its comorbidities were highlighted.(DOCX)Click here for additional data file.S3 TableThe common biological pathways between psoriasis and its comorbidities are highlighted.(DOCX)Click here for additional data file.


Express3D is a network analysis tool designed for the visualisation and analysis of graphs derived from biological data. It has proved to be powerful in the analysis of gene expression data, biological pathways and in a range of other applications. In version 3.2 of the tool we have introduced the ability to import, merge and display pathways and protein interaction networks available in the BioPAX Level 3 standard exchange format. A graphical interface allows users to search for pathways or interaction data stored in the Pathway Commons database. Queries using either gene/protein or pathway names are made via the cPath2 client and users can also define the source and/or species of information that they wish to examine. Data matching a query are listed and individual records may be viewed in isolation or merged using an ‘Advanced’ query tab. A visualisation scheme has been defined by mapping BioPAX entity types to a range of glyphs. Graphs of these data can be viewed and explored within BioLayout as 2D or 3D graph layouts, where they can be edited and/or exported for visualisation and editing within other tools.BioLayout There are many resources that store such data: at the time of writing, the website pathguide.org4 listed 547 pathway and interaction databases. However, many of these resources store the data in idiosyncratic formats and as a result it has been difficult for resources to exchange data between them.There has been an explosion in the amount of publicly available pathway and interaction data in recent years, derived from high-throughput experimental techniques, such as two-hybrid systems, mass spectrometry, phage display etc., or through focused studies and manually curated from the literature into pathway models5, CellML6 and BioPAX7. Of these, BioPAX is one of the most widely adopted data exchange formats. BioPAX is a community standard ontology for describing pathway and protein interaction data, suitable for qualitatively representing the current knowledge of biological systems. Seventy-four of the resources listed by the PathGuide currently support BioPAX, including some of the most widely used resources. BioPAX is overseen by the Computational Modeling in Biology Network (COMBINE) (http://co.mbine.org/) and has been released in major versions referred to as levels. The latest release is BioPAX level 3, version 1.0.To address this problem, there have been a number of efforts to standardize the exchange of pathway and protein interaction data from disparate sources, including PSICQUIC8 is a publicly available resource that aggregates and integrates pathway data from multiple organisms and databases into a common BioPAX language linked data representation. Data stored within this resource are currently derived from the databases ChEBI9, UniProt10, Reactome1, Pathway Interaction Database11, PhosphoSite10, HumanCyc12, HPRD13 and PANTHER2. The Pathway Commons website provides query and bulk download of these data. The system also makes these data available via a REST web service API, which provides programmatic access to data over the web. Pathway Commons has recently been upgraded, supporting BioPAX Level 3 and providing advanced graph queries via the CPath2 REST API.Pathway Commons14 has support via plugins (known as “apps” in Cytoscape version 3)16. The BiNoM plugin17 can import BioPAX Level 3 OWL files, the CyPath2 plugin is able to import and visualise BioPAX from the Pathway Commons resource, and the ChiBE pathway editor18 allows users to visually edit BioPAX pathways. CellDesigner, a graphical pathway editor, can export pathways as BioPAX19 and users of the R statistical programming language can access BioPAX via the rBiopaxParser package20. As BioPAX is a language based on the semantic RDF/OWL standard, it can also be edited using standard ontology authoring tools such as Protégé and WebProtégé21. However, it should be noted that there are various compatibility issues with some of the above, with different apps/tools being specific for different versions of the tools or BioPAX.A range of software tools already supports BioPAX use and exchange. For example the network analysis tool CytoscapeExpress3D23 that now supports querying of the Pathway Commons resource, allowing the user to pull in the results of specific gene/protein- or pathway-centric queries, and to visualise the results in a graphically intuitive manner.There is considerable interest in BioPAX data from the bioinformatics community and a growing interest in tools that support its visualisation and analysis. Here we report the implementation of a simple-to-use graphical interface within the network analysis tool BioLayoutExpress3D version 3.2 has been developed to open BioPAX Level 3 OWL files and generate network graph visualisations of BioPAX encoded pathway or protein interaction data. A web service client has also been developed within BioLayout to query Pathway Commons and import BioPAX networks directly.BioLayout24, an open-source Java library for developing BioPAX applications, has been incorporated into BioLayout. When a BioPAX OWL file is opened, it is parsed using PaxTools and an in-memory object model containing the elements in the BioPAX document is created. If the BioPAX major version is lower than Level 3, the object model is upgraded to Level 3 before the graph is constructed. The program iterates through each entity, looking up and assigning a shape for that entity type then creating a corresponding graph node. The program then connects nodes by creation of edges to represent components that are members of a complex, steps of a pathway and participants of an interaction.PaxToolsExpress3D communicates with Pathway Commons via the cPath2 REST API, sending commands using the PaxTools library. Using a search dialog . This value is obtained by counting the results of a traverse query of interactions within the selected pathway.Search results are displayed in a table. Clicking on a search hit displays an excerpt from the description with the highlighted search term, the persistent Uniform Resource Identifier (URI) of the search hit on Pathway Commons, the number of interactions (for pathways only) and the species name, which are displayed in a panel alongside the table of hits. As the BioPAX document defines species using Identifiers.orgAdvanced tab by double-clicking the row  pathway modelsRNA-Region. Some concepts did not have an exact analogue. In the case of the BioPAXSmall Molecule type, the equivalent concept in mEPN could either beIon/Simple Molecule orSimple Biochemical; theIon/Simple Molecule glyph was used. The BioPAX ontology has a hierarchical structure with increasing levels of granularity. Some glyphs were added to mEPN to handle generic BioPAX types where the more detailed type is not available in the data, such as aControl transition. Mappings between BioPAX/mEPN concepts and the 2D and 3D shapes used to represent them are shown inSome BioPAX concepts could not be mapped to the existing mEPN scheme so new glyphs were added. For example, a dumbbell shape was added for29. FMMM is a force-directed layout algorithm, introduced in BioLayout version 3.1 that allows graph layout to be computed highly efficiently, with a small number of iterations. The algorithm produces elegantly laid out graphs  layout algorithm, implemented within BioLayout, for use with BioPAX networks graphs  in both Class Viewer23 is used as an inspector for the graph. TheClass Viewer enables a graph to be sub-categorized into classes, based on node type. The classes taken together form aClass Set. During the graph construction process, a Class Set is created for BioPAX features and as the graph nodes are created, each node is assigned to a class with the name of the BioPAX entity type to which it corresponds  and then, assuming that the node identifiers are the same between the two networks, imported (File -> Import -> Class Sets…) whilst visualising a BioPAX network. In this way, the genes of interest in the expression network can be highlighted on the interaction network orvice versa.TheExpress3D version 3.2, thereby leveraging a powerful tool to support the visualisation and analysis of large pathway and protein interaction networks. The data stored in the Pathway Commons resource may now be easily searched and hits combined. The resulting networks can be displayed in 2D or 3D using a graphical display language that differentiates between the entity types described in the BioPAX hierarchy. Within the tool, the graphs can be explored and edited and where necessary exported for visualisation within other tools.There is now a wealth of pathway and protein interaction data in the public domain, collected and curated at great expense. However, accessing and using these data has proved challenging for many due to the lack of standard formats for data exchange between resources. The BioPAX standard has gone a long way to resolve this issue and has been widely adopted by the community. The Pathway Commons database has therefore been able to amalgamate the information stored in a number of the main pathway/interaction resources, making the information available through the CPath2 web service. Here we report our implementation of data query and import functionality within BioLayouthttp://www.biolayout.org.Software and source code are available fromhttp://dx.doi.org/10.5281/zenodo.1221630http://www.gnu.org/copyleft/gpl.htmlGNU Public Licence version 3 General Comments:Navigation in large/dense networks: Users often want to zoom in to a particular node/region of the network to view the connections and structures. It is difficult to achieve this using right-mouse button. A better approach may be to add support for three-wheel mouse – point the mouse to the node and then scroll to zoom in to the pointer direction.Enhancing support for node search in a dense network:Users should be able to click to highlight AND zoom in to it.Better scaling control (esp. for 2D view):Users would expect that node overlapping issue would be resolved by zooming in to the region. This is not the case during testing. Rather than simply getting bigger in size, both the sizes and relatives distance between nodes should be scaled properly when users zoom in. If there are some advanced options to achieve this effect, the default parameters should be improved. The BioLayout Express 3D is a very useful tool, especially its unique 3D rendering capability. The supports for importing network files in BioPAX level 3 format and integration with Pathway Commons appear to work well as described in the paper. My comments are mainly concerned with network visualization.Bugs On the 2D view, select and drag a node to a new position, the mouse pointer and the node are not well synchronized during the process.  Black screen also occurs sometimes. In addition to comparing their abilities in supporting BioPAX format, a general discussion on network visualization features of these popular tools will be very useful for new users to make informed choices.The standalone tools have the advantage for dealing with large data. The current tool can be improved with regard to more layout options and better performance for large networks.Given the increasing popularity for web-based visualization, the support for exporting the graph for D3.js rendering is certainly a welcome step. An online version for 3D network visualization (maybe based on WebGL?) will be highly received by the community. Additional comments / wish list:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors describe BioPAX loading and visualization support for the established BioLayout Express 3D network visualization software. As far as I know, this is the only software capable of 3D display of BioPAX derived networks. The new feature seems to work very well as described. I have a few minor comments to improve the manuscript.“the persistent Uniform Resource Identifier (URI)” – URIs in Pathway Commons are not guaranteed to be consistent between releases. They can be persistent if they are externally standardized e.g. uniprot IDs, but internal pathway commons URIs may change from release to release.The manuscript would benefit from one biological use case. The highlighting expression data paragraph is interesting, but including a biological example would help potential biologist users know how to use the software for a particular analysis.It would be nice to see a future directions section e.g. is there a plan to visualize SIF networks derived from BioPAX?It would be useful to briefly mention some examples of information that is not covered in the BioPAX to mEPN mapping e.g. post-translational modifications on proteins, the directionality of edges e.g. that could distinguish between left and right participants of an interaction vs. all participants of an interaction. Some of this information is possible to show in other visualization options, such as the Systems Biology Graphical Notation, which the authors may want to reference. Minor notes:The top pathways feature requires entering a keyword before the Search button is active, but with this query, it seems that a keyword is not necessary, as any query always shows all top pathways.A number of nodes have ‘null’ labels e.g. if they are interaction nodes. It would be nice to be able to suppress these labels somehow.It would be useful to see other information associated with the nodes e.g. data source, links to external databases. Some of this information is already shown in section 6 in panel A of the import network dialog shown in Figure 1.Is it possible to choose another layout after a network has been loaded?Bug: Clicking the ‘i’ information button on the right hand button tool bar brings up a help dialog called the ‘navigation wizard’. The links to PDFs at the bottom of this screen go to non-existent web pages.The edge names are all ‘0’, but could be the type of edge. Software comments:“within BioLayout, the user may refine searches by keywords, species, data source and BioPAX type” -> BioPAX class type.Minor text fixes:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Biomedical studies need assistance from automated tools and easily accessible data to address the problem of the rapidly accumulating literature. Text-mining tools and curated databases have been developed to address such needs and they can be applied to improve the understanding of molecular pathogenesis of complex diseases like thyroid cancer.We have developed a system, PWTEES, which extracts pathway interactions from the literature utilizing an existing event extraction tool (TEES) and pathway named entity recognition (PathNER). We then applied the system on a thyroid cancer corpus and systematically extracted molecular interactions involving either genes or pathways. With the extracted information, we constructed a molecular interaction network taking genes and pathways as nodes. Using curated pathway information and network topological analyses, we highlight key genes and pathways involved in thyroid carcinogenesis.https://github.com/chengkun-wu/PWTEES.Mining events involving genes and pathways from the literature and integrating curated pathway knowledge can help improve the understanding of molecular interactions of complex diseases. The system developed for this study can be applied in studies other than thyroid cancer. The source code is freely available online at  Biomedical literature is a primary knowledge source for life science research, which facilitates the information and knowledge exchange through various biomedical studies. PubMed, the largest collection of biomedical literature, now contains over 24 million records . In the This massive amount of available literature and its unstructured nature make it virtually impossible for researchers to keep track of all published results manually. Consequently, (semi-) automated methods and systems are needed to assist in the extraction of information and the reconstruction of knowledge. Text mining (TM) systems enable systematic collection of "scattered pieces" of information recorded in the biomedical literature ,3. This Curated databases constitute another important source of knowledge for biomedical studies. For instance, the 2014 Nucleic Acids Research online Molecular Biology Database Collection lists 1552 databases for molecular biology . SpecifiIn this paper we present a methodology for constructing a comprehensive molecular interaction map of a disease by integrating the results of text mining with curated data for biological pathways. While current systems mainly focus on events involving genes and proteins only, our networks include both genes and pathways as nodes, where edges correspond to different interactions between them. Pathways in particular represent biological function organized temporally, and are therefore an important actor in interaction networks. We collect data about pathway interactions from the literature by expanding a state-of-the-art system for event extraction, the Turku Event Extraction System (TEES) . The extTo demonstrate the potential of combining gene and pathway interactions, we use thyroid cancer as a case study. Thyroid cancer is the most common endocrine malignancy  and its Several efforts in mining complex and specific molecular events have been proposed ,22,23. IA typical event extraction system will need to have multiple components: NER modules, parsing , and relation extraction . Example event extraction systems include EventMine  and TEESSeveral datasets have been produced by applying event extraction tools to biomedical literature . EVEX,While most of the current work focuses on gene/protein events, pathways have not yet been integrated into event extraction systems. Still, as an important concept, biological pathways have been frequently mentioned in the literature. Consider, for instance, the following sentenceNotch pathway is activated by MAPK signalling and influences papillary thyroid cancer proliferation. (PMID: 23544172)Notch pathway and MAPK signalling). However, existing EE tools would only detect events related to genes, and will ignore pathways. Even more, from the above sentence they might extract that the Notch gene is activated by MAPK, which is incorrect given that the interplay is between two pathways rather than two genes.This sentence expresses a relationship between two pathways , and epidermal growth factor receptor (EGFR), has been approved by FDA as a drug for medullary thyroid cancer. More potential targeted drugs are under investigation or being tested in clinical trials  AND human[mh] AND english[la]) OR thyroid[ti]) AND )", as suggested by the National Cancer Institute (http://www.cancer.gov/types/thyroid). The results were limited to human studies in English.The corpus used in this study contains 38,572 abstracts from MEDLINE, as described in . It was http://top500.org/featured/top-systems/tianhe-2-milkyway-2-national-university-of-defense/). We carried out tests with 1,000 randomly selected abstracts from MEDLINE: the processing time was less than 3 minutes using 200 processes , as depicted in Figure It is a computationally time consuming process to perform event extraction on such a large collection of documents. To improve efficiency, we employed parallel processing. We implemented the whole pipeline of PWTEES on the world's fastest supercomputer Tianhe-2 built by the National University of Defense Technology  to evaluate the precision of PWTEES on pathway event detection; secondly, to estimate recall, we constructed a set of 100 <pathway, gene/protein> pairs (named as PR_TEST), which were randomly sampled from all possible <pathway, gene/protein> pairs satisfying the following two conditions: (1) the entities in one pair appear in the same document; (2) their distance is smaller than 100 characters . The PR_TEST set is used to estimate the recall of PWTEES , given by:We evaluated PWTEES in two ways: firstly, we randomly sampled 100 reported pathway events . Some of the errors were also present in TEES. For instance, in example #4 in Table MAPK.We analysed several typical types of error in the pathway events reported by PWTEES, as listed in Table Dkk-1 inhibits "the survival and migration of human PTC cells by regulating Wnt/β-catenin signaling and E-cadherin expression". However, PathNER reports "Wnt/β-catenin signaling" and "Wnt/β-catenin signaling and E-cadherin expression" at the same time, which was not correct as pathway mentions should not overlap. In the same example, the event type was incorrect, which could be possibly caused by the complex structure of the sentence and the presence of multiple event trigger keywords .We also noticed several PathNER errors. In example #2 in Table CD40 and Fas), while PWTEES highlights that CD40 can inhibit Fas-mediated apoptosis. We can further see cases where the biological semantics is better represented, like in example #4. The emphasis of the sentence in example #4 is placed on the importance of the "Ras/ERK1/2/ELK-1 and STAT3 pathways". This is well captured by PWTEES. On the contrary, EVEX reports a sub-event of the up-regulation of c-fos promoter, thus missing the main finding expressed in the original sentence. In addition, if a gene name is embedded in a pathway name, TEES will only pick up the gene mention. This will result in a loss of information as a pathway mention refers to a different and more complex entity. Consider, for example, a pathway event extracted by PWTEES depicted in Figure uPAR" and three gene names including FAK, PI3K and Akt. However, "FAK/PI3K/Akt signaling" is a single (pathway) name. PWTEES takes this correctly into account and reports the following event:In order to estimate the impact of removing some of the recognised gene names when they are part of pathway names, we compared the PWTEES results on the two test sets (P_TEST and PR_TEST) against the EVEX data. Example events that have been reported differently by EVEX and PWTEES are listed in Table FAK/PI3K/Akt signalingTheme: uPARCause: Downregulation of Type: Negative regulationhttp://evexdb.org/download/network-format/Metazoa/Homo_sapiens.tar.gz).With the parallel processing mentioned previously, we ran PWTEES on the whole thyroid cancer corpus. The number of unique interactions detected is listed in Table To explore whether adding pathway information could provide a more comprehensive genetic space, we checked the genes involved in those interactions and compared those to the thyroid cancer related genes (TC-genes) extracted in our previous study . A total· EVEX-genes: genes that participate in the EVEX interactions.· PWTEES-NON-PW-genes: genes explicitly involved in non-pathway PWTEES interactions;· PWTEES-PW-genes: genes involved in pathway PWTEES interactions and all genes in mentioned pathways;· TC-genes: genes linked to thyroid cancer ;The result is depicted in Figure We constructed two networks as undirected graphs from the interactions mentioned above. The MERGE-PW network takes both genes and pathways as nodes, constructed as described in the Methods section. It contains 576 nodes and 3136 edges , one of the most common mutated genes for thyroid cancer [MYC (ID: 4609) was highly ranked in the TC-genes but it has only one neighbor in NON-PW. This highlights the importance of integrated pathway data.In MERGE-PW, the top 10 hubs are all pathways, as listed in Table d cancer , does noPIK3R2 (ID: 5296) and TNF-alpha (ID: 7124). PIK3R2 is a regulatory component of PI3K, a major member of the PI3K/Akt pathway, one of the top hubs in the MERGE-PW network  containing both the MERGE-PW network and the NON-PW network in *.zip format.Click here for file


Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets . We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72  and 1.71 , respectively. These results could provide a more complete picture of the lung cancer pathogenesis. Lung cancer, especially non-small cell lung cancer (NSCLC), is the most common cause of cancer-associated mortality worldwideEGFR and KRAS and gene fusion detection of ALK have been widely used in clinical practiceThe current clinico-pathological staging system is not adequate5Although previous gene expression microarray studies have shown a statistically significant association of the expression of many individual genes with disease, the findings usually lack biological meaning12Classification of patient risk using a single biomarker that is strongly associated with disease outcome might not be a good strategy, as a dysregulated gene that may not show any obvious association with disease on its own may interact with others in the same pathway, resulting in carcinogenesis or drug resistance1415CSNK1A1 and MYST4) had significant HRs in all four datasets. After combining the results for the four datasets, a total of 2418 genes showed a significant HR (data not shown). When pathway mapping was used to identify survival-associated biological pathways based on these 2418 genes, 15 pathways in which these genes were enriched were identified (The study population consisted of 443 patients with lung adenocarcinoma from the University of Michigan Cancer Center (UM) n = 178), the Moffitt Cancer Center (HLM) (n = 79), the Memorial Sloan-Kettering Cancer Center (MSK) (n = 104) and the Dana-Farber Cancer Institute (CAN/DF) (n = 82)8, the Moentified .To evaluate the impact of signatures in a given pathway on survival, a pathway-based risk score was calculated for this pathway and used, together with survival analysis, to evaluate prognostic ability. Survival analysis showed that three pathways were significantly associated with survival in all four datasets . Twenty-/RAGE signalling pathway-based signature containing 21 differentially expressed genes and defining high-risk patients as those with a pathway-based risk score higher than the median, high-risk patients had a significantly shorter median survival than low-risk patients in the CAN/DF dataset , the HLM dataset , the UM dataset  and the MSK dataset .As shown in Similar results were obtained using the beta-adrenergic receptor regulation of the ERK pathway-based signature  or the cThe survival pattern for patients with each of the three pathway-based signatures in the different datasets was interesting. In the CAN/DF dataset, the high-risk group, based, respectively, on the HMGB1 / RAGE signaling pathway, beta-adrenergic receptor regulation of ERK pathway and clathrin-coated vesicle cycle pathway, had a median survival of 71, 38 and 37 months, while the low-risk group did not reach median survival. In the HLM dataset, the high-risk group had a median survival of about 20 months , while that of the low-risk group was greater than 45 months . In the UM dataset, the median survival for the high-risk group was 42, 45 and 48 months, almost 3 times lower than that in the low-risk group . In the MSK dataset, the median survival for the high-risk group was around 50 months , while the low-risk group survived longer .To evaluate the prognostic effect of the combined risk scores for these three pathway-based signatures, a CPBR score was computed by linear summation of each of the three pathway-based signature scores multiplied by the weighting coefficient for that pathway obtained by Cox proportional hazards regression. Patients in the high-risk group (risk score higher than the median) had a significantly shorter median survival than those in the low-risk group in all four datasets  .Results of multivariate Cox proportional hazards regression showed that stage effect had significant adjusted HR in each dataset and can be mentioned as an independent prognostic factor . After eMultivariate Cox proportional hazards regression was used to examine the predictive power of 2418 genes selected from the inverse-variance weighting method. Numbers of significant genes in four datasets are 820, 574, 429, and 622, respectively. Range of significant HRs was from 0.05 to 11.3 and had high variation between four datasets . ConsideThree pathway-based signatures and the CPBR score were validated in two independent cohorts (GSE14814 and GSE13213). Patients with high risk identified from the HMGB1 / RAGE signalling pathway, the beta-adrenergic receptor regulation of the ERK pathway, the clathrin-coated vesicle cycle pathway, or CPBR score had significant shorter overall survival , age, three pathway-based signatures, and CPBR score had significant HRs. Adjusted HR of CPBR score was 2.72 (95%CI = 1.80 to 4.11 and p < 0.0001). In addition, stage, three pathway-based signatures, and CPBR score had significant HRs in the second validation cohort (GSE13213). Adjusted HR of CPBR score was 1.71(95%CI = 1.45 to 2.03 and p < 0.0001) .In this study, multivariate Cox proportional regression analysis was used to identify potential survival-associated genes in each of the 4 National Cancer Institute datasets, then inverse-variance weighting method was used to combine the results from the four datasets. The use of this method increases the statistical power and provides more robust resultsCSNK1A1 and MYST4, were found to be significantly associated with survival in all four datasets. Since the results for the four datasets were not very consistent, we used inverse-variance weighting method to combine the four sets of results to increase statistical power. After pathway analysis, pathway-based risk scores were computed using the level of expression of genes in the same pathway weighted by regression coefficients. Each of the pathway-based scores was found to be a good predictor of clinical outcome. Subjects with a higher pathway-based risk score for a given pathway were classified into the high-risk group based on that pathway. The results showed that 21 of the 65 genes in the HMGB1 / RAGE signalling pathway, 22 of the 70 in the beta-adrenergic receptor regulation of ERK pathway and 31 of the 107 in the clathrin-coated vesicle cycle pathway were significantly associated with clinical outcome of lung cancer. The high-risk group defined using the risk score based on any one of the three individual pathways had a shorter overall survival than the low-risk group.In the Cox regression analysis, only two genes, We then developed a simplified CPBR score to combine the effects of these three signatures, and the results showed that the high-risk group had even shorter overall survival than the low-risk group and that the CPBR score gave a better outcome prediction of outcome of lung adenocarcinoma patients. These results showed that differentially expressed genes in the same pathway might interact with each other and contribute to a worse prognosis.Some studies have reported that cellular pathway signatures can be useful for treatment development, prognosis prediction and subtype classification in lung cancer1418162227In studies using the same strategy and similar clinical and pathological features and treatment protocol, not all patients with lung cancer show the same clinical outcome and sensitivity to treatment because of the extreme heterogeneity of tumoursThe limitation of the gene expression profiling or pathway based approach is applications in the clinical practice. The IHC method or FISH method with convenient and low cost of assays is well used in the clinical practice. However, it can be only applied in testing few genes. Several technologies including the multiplexed quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) or digital PCR may provide solutions for the clinical practice of the pathway based signatures. However, it still needs to be evaluated in the future study.Gene-expression profiling has been used to explore biomarkers associated with subtypes of lung cancer, overall survival and recurrence of cancer306323316Through risk assessment by pathway-based signatures, patients had high risk estimated from pathway based signatures may need to receive different treatments. For example, patients with completely resected stage I NSCLC was recommend for no adjuvant chemotherapySeveral limitations of the microarray data are also needed to be considered. First, the high variations of gene expression data were obtained from different microarray platforms due to different probe designs or signal detection methods. Hence, the comparisons between different microarray platforms have to be concerned. Second, the stromal cell contamination varies among dataset and samples, which would give variations in micro array-based analyses.EGFR, KRAS, and ALK fusion. It is difficult to estimate the effect of the CPBR in patients with major genomic abnormalities. Additionally, the MetaCore software used enrichment analysis to find the overrepresented pathways and pathways with the smaller size of the gene set might have more chance to reach significance. It is the weakness of the enrichment analysis. Because the false positive may be introduced from the limitation of the MetaCore software, several steps were used to reduce the bias. In the first step, the Bonferroni correction method was applied to reduce potential false positive. There were more than 1000 pathways in the database of the MetaCore. The significant level was corrected from 0.05 to 10−5. In the second step, gene-signatures of significant pathways were evaluated in four training datasets. Pathways had significant associations with survival were kept. Finally, candidate pathway-based signatures were validated in two independent cohorts.A potential limitation of this study is that the public datasets used in this study did not provide the information of treatment response and major genomic abnormalities such as Our analysis method of gene expression profiling identified pathway-based signatures closely correlated with clinical outcome of lung adenocarcinoma. We have derived a simple CPBR score which may improve the accuracy of outcome prediction for lung adenocarcinoma, as the CPBR score showed a higher correlation with clinical outcome than gene- or individual pathway-based scores. These results provide a more complete picture of the pathogenesis of lung carcinoma and provide direction for future studies. The CPBR signature may be useful in stratifying subpopulations of lung adenocarcinoma for clinical outcome prediction and individualized therapies for lung adenocarcinoma patients.https://array.nci.nih.gov/caarray/project/details.action? project.id= 182.The study population consisted of 443 patients with lung adenocarcinoma from the University of Michigan Cancer Center (UM) n = 178), the Moffitt Cancer Center (HLM) (n = 79), the Memorial Sloan-Kettering Cancer Center (MSK) (n = 104) and the Dana-Farber Cancer Institute (CAN/DF) (n = 82)8, the MoGSE14814 in the GEO database, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14814) and dataset (n = 117) from the Nagoya University in Japan  were used to validate our findings.An additional published dataset (n = 71) from the University of Toronto , and the corresponding p value was estimated by Cox regression analysis, giving 4 HR and 4 p values for each gene, then the inverse-variance weighting method was used to combine the HRs from the four datasetsTM version 6.13 . Because more than 1000 pathways are included in the MetaCore database, the Bonferroni correction was used to avoid multiple testing issues. For each of the 15 pathways found to contain differentially expressed genes, a pathway-based risk score was calculated for each subject in each data set to determine the impact of the pathway on the prognosis. The risk score was a linear combination of the values for the expression of each gene in the pathway multiplied by a weighting value for each gene estimated using Cox proportional hazards regression. Using this approach, we found signatures in three pathways that were significantly associated with survival in lung adenocarcinoma. A combined pathway-based risk (CPBR) score was then derived by linear combination of the pathway-based risk score for each of the three pathways multiplied by a weighting value for that pathway estimated using Cox proportional hazards regression. We then verified this CPBR score using an independent cohort.Genes that were differentially expressed and associated with survival were further analysed for biological function or involvement in different pathways using pathway maps of the MetaCoreIn the pathway-based study, the pathway-based median risk score was taken as the cut-off point for high or low-risk group classification, while, in the CPBR score study, the median of the CPBR score was used as the cut-off. The Kaplan-Meier method was used to generate survival curves and the difference between survival curves was evaluated using the log-rank test. All tests were two-tailed, and p values less than 0.05 were considered to be significant.Both survival analysis and calculation method of pathway-based scores for three selected pathways and the CPBR score were the same as previous section described. HR of the CPBR score was estimated from multivariate Cox proportional hazards regression with the clinical covariates of age, sex, stage, and treatment method after operation.How to cite this article: Chang, Y.-H. et al. Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma. Sci. Rep. 5, 10979; doi: 10.1038/srep10979 (2015).