PathBank aims to provide a pathway for every protein and a map for every metabolite. This resource is designed specifically to support pathway elucidation and pathway discovery in transcriptomics, proteomics, metabolomics and systems biology. It provides detailed, fully searchable, hyperlinked diagrams of metabolic, metabolite signaling, protein signaling, disease, drug and physiological pathways. All PathBank pathways include information on the relevant organs, organelles, subcellular compartments, cofactors, molecular locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to the rich data contained in public chemical databases such as HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All PathBank pathways are accompanied with references and detailed descriptions which provide an overview of the pathway, condition or processes depicted in each diagram. Every PathBank pathway is downloadable in several machine-readable and image formats including BioPAX, SBML, PWML, SBGN, RXN, PNG and SVG. PathBank also supports community annotations and submissions through the web-based PathWhiz pathway illustrator. The vast majority of PathBank's pathways (>95%) are not found in any other public pathway database.PathBank ( Pathway diagrams are the roadmaps for molecular biology. Just as roadmaps show the connections between villages, towns, and cities, pathway diagrams illustrate the connections between genes, proteins and metabolites. A well-illustrated pathway diagram provides biological context to complex molecular processes in an easily understood and highly visual manner. In this regard, pathway diagrams provide a remarkably useful means for scientists to share, integrate, interpret and visualize ‘omics data and ‘omics measurements. Even now, compilations of pathway diagrams (i.e. pathway databases) are among the most frequently used and oft-referenced resources in the entire field of bioinformatics. This is because pathway databases are widely called upon to interpret genomics, metagenomics, transcriptomics, proteomics and/or metabolomics data. Furthermore, they are frequently used to facilitate more complex analytical tasks such as cellular modeling , flux bahttps://cgap.nci.nih.gov/Pathways/BioCarta_Pathways)  , InnateDathways) , PharmGKathways)  and the athways) , which ehttps://portal.genego.com/), Protein Lounge (http://www.proteinlounge.com/) or IPA  . In particular, the Ingenuity Pathway Analysis system by Qiagen is perhaps the most popular and most extensive commercial resource in terms of pathway coverage and pathway analysis tools.In addition to these freely available or open-access pathway databases, there are also several well-known commercial pathway databases such as GeneGo . PathBank provides detailed, fully searchable, hyperlinked diagrams of metabolic, protein/metabolite signaling, disease, drug and physiological pathways. Its pathway diagrams include information on the relevant organs, organelles, subcellular compartments, locations and structures to provide important biological context. All molecular/protein images are hyperlinked to databases with rich descriptions and all pathways are viewable using a modern ‘Google Maps’-like or AJAX-based visual navigation system. All of PathBank's pathways have detailed descriptions and all of its images, image maps, descriptions and reactions are freely downloadable in several common data exchange formats. The entire database may be browsed, searched or annotated using a variety of easy-to-use tools. Likewise, community or crowd-sourced annotations and submissions to PathBank are supported through the web-based PathWhiz  pathway Homo sapiens, Bos taurus, Rattus norvegicus, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Saccharomyces cerevisiae, Escherichia coli and Pseudomonas aeruginosa). The database currently contains 78 488 different compounds , 8993 different proteins and describes more than 176 535 different reactions and interactions. PathBank pathways are divided into two broad categories or classes: (i) Metabolite/Compound pathways and (ii) Protein pathways. Metabolite/Compound pathways contain a majority (>50%) of small molecules compared to proteins as pathway entities. Protein pathways contain a vast majority of proteins  compared to metabolites as pathway entities. In the current version of PathBank there are 109 836 Metabolite/Compound pathways and 398 Protein pathways.PathBank (version 1.0) contains 110 234 richly annotated, fully colored, machine-readable pathways for 10 model organisms (Within PathBank there are six subcategories of Metabolite/Compound pathways: (i) Metabolic (catabolism/anabolism); (ii) Physiological/Endocrinological (primarily involving metabolites); (iii) Metabolite Signaling; (iv) Drug Metabolism; (v) Drug Action; and (vi) Disease . In contrast to the Metabolite/Compound pathways there are 15 subcategories of Protein pathways: (i) Immunological; (ii) Cellular Response; (iii) Gene Regulatory; (iv) Growth Factor; (v) Cytokine Signaling; (vi) Protein/Peptide Hormone-Mediated; (vii) Neurological Signaling; (viii) Developmental Signaling; (ix) Kinase Signaling; (x) Apoptosis Signaling; (xi) Stress Activated Signaling; (xii) Pathogen-Activated Signaling; (xiii) Transport/Degradation; (xiv) Cytoskeletal Signaling; and (xv) Disease (primarily involving protein dysregulation).All pathways have detailed text descriptions that provide summaries of the pathway or the processes being depicted. Averaging a length of 342 words, these original descriptions highlight relevant background information, biological context and key reactions. Similarly, all compounds in PathBank have detailed text descriptions averaging 305 words. Further, all compounds have manually curated abbreviations for use in the easier-to-read large font pathway visualizations. Most of these compound names, abbreviations and descriptions were obtained from our own locally maintained databases such as HMDB , DrugBanhttps://www.google.com/maps). PathBank also has a variety of tools to facilitate the display of specific pathway components and to map their relative concentrations. Similarly, different types of pathway displays are possible to facilitate printing or the preparation of slides and/or images. All of PathBank's pathway images, image maps, descriptions and reactions are freely downloadable in several common machine readable or data exchange formats including BioPAX, SBML, PWML, SBGN, RXN, PNG and SVG.PathBank supports a wide variety of searching and browsing functions, including pathway browsing/searching (by name), compound browsing/searching (by name), protein browsing/searching (by name), sequence searching, compound similarity searching, molecular weight searching and advanced (Boolean) text searching. Users may also search PathBank by using lists of compound names, protein names and/or gene names or their corresponding identifiers . All of the pathways, proteins, and metabolites in PathBank are hyperlinked to other resources and all of the pathways are navigable through a modern visualization interface modeled after Google Maps  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. PathBank uses the model–view–controller architecture, in which internal data logic is separated from user input and data presentation. The raw information stored in the database is dynamically extracted and rendered into web pages by PathBank's HTML interface responder. PathBank is hosted on a Digital Ocean server equipped with 4 CPUs, 50 GB of disk space, 8 GB of RAM and an additional 200 GB storage space on an Amazon S3 storage facility.PathBank was implemented using a Ruby on Rails  molecules. Converting these data into ‘generics’ or compound classes leads to a significant loss of information. Furthermore, recent work in lipidomics is highlighting how important individual lipid molecules are for certain biological functions or disease processes . This isA screenshot montage of the PathBank interface and its search and browsing tools is shown in Figure If users choose to browse by Pathway, a multi-page table is presented that contains four columns: (i) the PathBank identifier (with the thumbnail image); (ii) the pathway details, which includes the pathway name, species, description and download links; (iii) the compounds (which are hyperlinked); and (iv) the proteins . Clicking on the thumbnail image of a given pathway will direct users to the full-size pathway image. Likewise, clicking on the metabolite/compound names (column iii) or protein names (column iv) will open a page with detailed compound descriptions  or protein descriptions (from UniProt).If the TOPP option is selected, a multi-page table is presented showing three columns: (i) the PathBank identifier; (ii) the pathway name including the species name ; and (iii) the link to the pathway view. In the TOPP, similarly named (replicated) pathways are grouped together to facilitate browsing of largely unique pathways. Groupings are represented by a single primary pathway, hence the table name, which has a clickable pathway name displayed in blue text. These hyperlinked pathways may be selected by the user to navigate to a popover view containing all of the related pathways which can be similarly browsed. In the case of lipid pathways, only the generic version of the pathway is listed in the main table; the pathways for the specific lipids are browsable in the generic lipid's popover view.Users can browse PathBank's compounds and proteins in much the same manner. Multi-page tables present compounds and proteins alphabetically in three columns: (i) the compound/protein's identifier along with the corresponding View buttons; (ii) the compound/protein's name (with a short description); and (iii) the associated pathways (which are hyperlinked). Clicking on a compound's View button will take a user to the appropriate ‘Compound Card’, corresponding to the database most appropriate for the chosen (or filtered) organism. The default is HMDB if no organism has been selected. Clicking on a protein's corresponding View button will take a user to the appropriate ‘UniProt Card’. Unlike the pathway browse results, compounds and proteins are filterable only by species using the dropdown menu provided. An additional filter in the form of a Search box at the top right of the table allows users to filter the page by compound, protein or pathway name.PathBank places a strong emphasis on providing users with high quality, artistically pleasing pathway diagrams that are not only correct and informative but also colorful, interactive and richly detailed. All of the pathway diagrams in PathBank use scalable vector graphics (SVG) and a web interface technology inspired by Google Maps. This allows rapid and continuous zooming using a mouse pad or a mouse scroll wheel or through simply clicking on-screen zoom icons. It also allows facile navigation around zoomed-in pathway diagrams through a simple click-and-drag operation or through clicking on-screen up/down or left/right arrows located near the on-screen zoom functions. A full-screen view of each PathBank pathway diagram is also available, which can be toggled off and on by clicking the full-screen icon located between the navigation arrows.On the right side of each pathway diagram is a pathway display panel with five tabs . The default view is the Description panel, which describes the pathway in detail and provides one or more literature references. The Highlight panel (viewed by clicking the ‘Highlight’ tab) allows users to select and color different proteins and metabolites for display purposes. The Analyze panel  allows users to enter concentration (relative or absolute) data on proteins/transcripts and/or metabolites and to have these colored on the pathway diagram. It is highly recommended that users switch to the black and white version of the pathway when using the highlight and analyze tools in order to clearly see the elements being colored. The download panel (viewable by clicking the ‘Download’ tab) allows users to select the type of file format that they wish to have their pathway image (annotated or untouched) saved as. Options are available for downloading images in BioPAX  image foIn addition to these browsing functions, PathBank also offers extensive search functions. These include a general text search (available at the top of every PathBank web page) as well as several, more specific, search functions listed under the ‘Search’ tab. The text search supports Boolean logic  along with field-specific searches covering compound names, protein names, compound/protein identifiers, pathway names, and pathway descriptions. Instructions for the text search are accessible using the Search dropdown list. There are four additional searches available: (i) the Path-MAP Advanced Search; (ii) the ChemQuery Structure Search; (iii) the Molecular Weight Search; and (iv) the Sequence Search. The Path-MAP search allows users to enter long lists of compound names, protein names, compound identifiers, protein identifiers or gene identifiers to search for organism-specific pathways enriched with these entities. The result is a list of pathways with enrichment scores calculated using the frequency of matches and the number of pathway entities as scaled using a hypergeometric function. It is also possible to enter similar lists with Path-MAP but with concentrations or relative concentrations, as might be obtained from a typical proteomics, transcriptomics or metabolomics experiment. The result of this type of search is a pathway annotated and colored with the corresponding metabolite/protein concentrations according to a yellow (low)-red (high) concentration gradient. Other searches such as the ChemQuery and Molecular Weight searches are intended for metabolite or compound searching against PathBank's chemical database of 78 271 compounds and are identical to those used by HMDB, SMPDB and many other databases. The Sequence search uses BLAST  to find PathBank's ‘About’ tab provides additional information about PathBank, its associated release notes, the required citations, database statistics, the PathBank style guide, links to other Pathway Databases, as well as images (via a Pathway legend) for the different components  seen in PathBank pathways. PathBank's ‘Download’ tab allows users to download pathways, metabolite names and protein names (in CSV files) as well as all of its pathways in BioPAX, SBGN, SBML and PWML format. A subset of PathBank's pathways (i.e. the primary pathways browsable through the TOPP) are also downloadable in SVG and PNG format. Due to the extremely large file size, users who wish to download all pathway images will need to submit a request using PathBank's Contact form. All of PathBank's sequences (both gene and protein) are available in FASTA text format, all of its chemical structures are available in SDF format and all of its reactions are available in RXN format.E. coli (ECMDB). PathBank extends these existing pathway collections to cover other model organisms and to include disease, physiological, protein and cellular signaling pathways. The PathBank curation team took advantage of a number of recent improvements to the PathWhiz pathway illustrator  to ensure it adhered to the PathWhiz style guides and SOPs. If the pathway failed to meet style guide requirements or failed to follow the SOPs, it would be corrected and re-evaluated until it met those requirements. Only after a pathway diagram passed secondary inspection would it be allowed to be propagated (to other species) or replicated (for related pathways within a species). This quality control protocol was applied to all hand-drawn primary pathways in PathBank which serve as template pathways that can be automatically replicated and/or propagated via PathWhiz. Spot checks (of ∼5% of the pathways) for any automatically replicated/propagated pathways were manually done by the lead curator(s) to ensure logical and biological consistency. Global or system-wide checks were also done periodically to ensure that organelle or organ/tissue images were appropriate for a given organism. Many of the primary pathways  that have been online for several years have undergone heavy remediations in order to comply with the guidelines outlined in PathBank's detailed style guide. As a result of this huge effort, these redrawn pathways have a standard, more aesthetically pleasing appearance, greater biological context (e.g. transporters across cell and organelle membranes) and increased connectivity between pathway entities. Many pathways have been updated with new descriptions and icons that have been drawn for PathBank's growing image collection. User feedback has also been used to further refine and improve these pathways.All members of the PathBank curation team were required to have at least an undergraduate degree in bioinformatics or molecular biology. This ensured that they had sufficient biological and/or biochemical knowledge to understand the scientific literature and the biological processes being illustrated. All curation team members were also given extensive training by the lead curator(s) in pathway illustration via hands-on mentoring, text instructions, peer support, and video tutorials. However, it is important to note that given the inherently artistic nature of pathway illustration, the differences in artistic/illustrative skills among the curation team and the complexity of some biological processes, it is impossible to guarantee that all pathways in PathBank exhibit the same level of visual or conceptual quality.Improvements and updates to PathBank's pathways are an ongoing process. Minor corrections or small improvements in image or pathway quality or pathway descriptions will be done without a formal update announcement. However, significant changes, additions or improvements to an individual pathway diagram (>10% in the number of pathway components), will be listed in the pathway description and the last updated date will be modified to reflect any such changes. As this is only version 1.0 of the PathBank database, all pathway diagrams are dated with August 2019 as the last update date. Large-scale updates and improvements to the database in the future will be given database version numbers  and suitable database update dates. They will also be described in detail as publications or online update descriptions as appropriate.PathBank is made available under the Open Database License in which users are free to contribute to the database using its PathWhiz pathway drawing tool. Indeed, one of the authors listed on this manuscript (MRG) contributed >50 primary pathways to PathBank as an external ‘community’ member. Of course, not all community contributions are of sufficient quality to merit inclusion to PathBank. Any decision to include/exclude community or crowd-sourced pathway submissions depends on the quality/quantity of the contribution and their adherence to PathBank's SOPs and style guide. The final decision for any external contribution is ultimately made by PathBank's lead curator. The PathBank curation team will closely work with, and will train, external pathway illustrators who are serious about contributing to this resource.Like all pathway databases, PathBank is a work in progress. Currently both the quantity and level of coverage of PathBank's protein pathways falls far short of what is currently available for PathBank's metabolite/compound pathways. Indeed, only 398 protein pathways have been illustrated to date, compared to 109 836 metabolite pathways. This imbalance reflects the past focus of the PathBank team's curation effort (which was primarily in metabolism), the extremely large number of lipid pathways (>90 000) and the challenges that exist in depicting the somewhat more complex protein signaling pathways. As PathBank's collection expands, so too will its genome coverage (currently ranging between 1.9 and 25.1%) for each model organism (viewable on PathBank's statistics page). Of course, even with this major annotation effort, not every known pathway will appear in PathBank—now or in the near future. Likewise, it is expected that a number of users will notice that some PathBank pathways do not depict all expected or relevant entities in the pathway diagrams. In these situations, we certainly encourage users to contact the PathBank curation team (via the ‘Contact’ tab) so that those corrections can be made or those PathBank depictions can be more fully explained.Danio rerio), the African clawed frog (Xenopus laevis), the chicken , rice (Oryza sativa), the malaria parasite , the sleeping sickness parasite (Trypanosoma brucei) and several bacteria, including Bacillus subtilis, Staphylococcus aureus, and Thermotoga maritime. Additional organisms or species may be added depending on user feedback.Another clear limitation with PathBank is the relatively small number of model organisms that are currently covered. Currently, only 10 organisms are included in PathBank's ‘zoo’ with several well-known model organisms, including a few key vertebrates, several parasites and a number of important bacteria, not being included. Over the coming two years, it is expected that at least nine other model organisms will be added to the database, including the zebrafish  that allows users to toggle between ‘regular text’ and ‘large text’ for both the full-color and simplified visualizations so that the image labels are more visible and printer-friendly. In order to accommodate the larger font size, manually curated abbreviations and gene symbols adhering to a strict seven-character limit are used in place of the longer full names of compounds and proteins. Additional efforts are ongoing to create even smaller or more compact pathway visualizations with less empty space and more grid-like entity layouts.PathBank represents a new type of pathway database. It has been designed to take some of the best features of earlier pathway databases and to combine them with newer, more modern kinds of web visualization tools and more advanced web interfaces to create a fully open access, visually rich and highly comprehensive pathway resource. PathBank also brings a number of unique features into the Pathway database field including exceptional pathway coverage, richly detailed pathway and entity descriptions, a number of new pathway types or categories , new kinds of pathway or entity visualization details  and a much wider variety of pathway viewing and data exchange/storage options. Being the first release of this particular pathway database, we expect that users will note some missing elements or inconsistences with certain pathways or pathway data. However, we anticipate that through user feedback, continued community engagement and ongoing curation efforts, the next release of PathBank will be even more comprehensive and potentially much more useful to the community.


Transcriptome data can provide information on signaling pathways active in cancers, but new computational tools are needed to more accurately quantify pathway activity and identify tissue-specific pathway features. We developed a computational method called “BioTarget” that incorporates ChIP-seq data into cellular pathway analysis. This tool relates the expression of transcription factor TF target genes (based on ChIP-seq data) with the status of upstream signaling components for an accurate quantification of pathway activity. This analysis also reveals TF targets expressed in specific contexts/tissues. We applied BioTarget to assess the activity of TBX21 and GATA3 pathways in cancers. TBX21 and GATA3 are TF regulators that control the differentiation of T cells into Th1 and Th2 helper cells that mediate cell-based and humoral immune responses, respectively. Since tumor immune responses can impact cancer progression, the significance of our pathway scores should be revealed by effective patient stratification. We found that low Th1/Th2 activity ratios were associated with a significantly poorer survival of stomach and breast cancer patients, whereas an unbalanced Th1/Th2 response was correlated with poorer survival of colon cancer patients. Lung adenocarcinoma and lung squamous cell carcinoma patients had the lowest survival rates when both Th1 and Th2 responses were high. Our method also identified context-specific target genes for TBX21 and GATA3. Applying the BioTarget tool to BCL6, a TF associated with germinal center lymphocytes, we observed that patients with an active BCL6 pathway had significantly improved survival for breast, colon, and stomach cancer. Our findings support the effectiveness of the BioTarget tool for transcriptome analysis and point to interesting associations between some immune-response pathways and cancer progression. Example systems of each generation are, respectively, GOstat2, GSEA3 and SPIA4. Notable in the 3rd generation is the use of pathway topology in generating the scores. In PT, prior knowledge of activation and suppression relationships captured in the gene/protein network is used to improve score estimation compared to the previous generation systems that merely used frequency of differentially expressing genes for the calculation, e.g., Fisher’s Exact test in GOstat and Kolmogorov-Smirnov statistic in GSEA. PT methods themselves evolved as different types of omics datasets become available. Isik et al.5 proposed a hybrid method in which both transcriptome and ChIP-seq data are combined to estimate activation/suppression levels of signaling networks. PARADIGM6 combines mutation and gene expression data using factor graph, to better assess pathway perturbation obtainable from cancer samples. APA7 aims to detect altered pathways by dynamically calculating pathway rewiring through analyzing correlation between genes, but this system does not use prior knowledge. Most recently, we reported our own PT methods which aim at identifying “pathway routes” as perturbed portion using a Bayesian model built from topological pathways9.A common approach for interpreting RNA-seq data is to assess the expression level of genes along a curated signaling pathway, and then “score” the pathway for its potential level of activity. The methods to score pathways have steadily evolved since the arrival of the first high-throughput expression technology “DNA microarray” in the 80s. Khatri What we report here goes beyond the aforementioned pathway scoring systems. Our goal is to extend “curated” pathways downstream of a transcription factor (TF) involved in a signaling network, i.e., TF target genes. This study’s objective stems from the observation that majority of existing signaling pathways are sparse in cataloging the events occurring inside nucleus where TFs bind DNA to regulate mRNA transcription. Our hypothesis is that these TF regulatory events are highly context-sensitive, meaning the regulatory events may heavily depend on the temporal and spatial context of the experimental condition. As such, the field has not tackled this problem yet. However, we conjecture that the recent availability of ChIP-seq data through ENCODE and other large scale genomics initiatives like TCGA offer opportunities to incorporate TF target genes into the pathway analysis.Figure Gi is concluded to be a target of the TF Tj, then how to gauge the impact of adding the relationship “Tj regulates Gi” into the pathway? We consider that the Kaplan-Meier (KM) survival analysis10, the non-parametric statistical method used to study the efficacy of treatments or conditions of cancer patients, can be used for developing such assessment method.There are a number of issues to be dealt with when attempting to extend pathways. The first issue is whether some pathways are more amenable to the type of TF downstream extension. Our choice is that immune signaling pathways are good candidates because of the established consensus on these immune pathways. The second issue is which transcriptome datasets is more appropriate to use for the pathway extension. As stated earlier, TF downstream identification can be context-sensitive and, as such, a sufficiently large number of “birds of a feather” transcriptome datasets should be used for a cohort-based analysis. The public TCGA datasets are good candidates since each cancer type can establish a context. The third issue is if there is any quantifiable measure that can guide the actual extension process. For example, if one gene 12. Indeed, our analysis outcomes show that the scores obtained using extended pathways effectively stratify patients into groups with different survival characteristics, supporting the value of the scoring system.Lastly, one will also have to show the impact of extending the pathway. When using TCGA datasets, the question can be answered by testing if our method can meaningfully stratify patients. For this evaluation, KM survival analysis can also be used, since the relationship between immune responses and cancer progression has been studied extensivelyet al.13, if the immune response is shifted towards Th2-dominance, lesion progression is favored. What we report here is how the three pathways known for regulating Th1, Th2 and T-fh cell differentiation can be extended and what the implications of these extensions are.Prior to presenting the computational analysis outcomes, some background of T cell differentiation is given. Figure In this experiment, we processed ENCODE ChIP-seq data to produce 1275 potential target genes of TBX21 using the method described in Section 4.1 . These potential target genes are input into the BioTarget pipeline, which was run for each of the five TCGA cancer datasets, Stomach Adenocarcinoma (STAD), Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Lung Adenocarcinoma (LUAD), and Lung Squamous Cell Carcinoma (LUSC). Table r = 0.86) in the breast cancer case (n = 1093). The second graph shows that the gene expression levels of TBX21 and its BioTarget identified Down gene TTC26 are negatively correlated (r = −0.26) in the breast cancer case. An additional ten correlation graphs are given later in Supplementary Information in order to document the high correlation between TBX21 and representative Up/Down targets identified in each of all five cancer cases.Next, we performed two types of post analysis, a correlation study to check the quality of the discovered targets and the KM-survival analysis to study the impact of extended pathways with newly added TF target genes. Figure Kaplan-Meier (KM) survival analysis is used here to evaluate what the extended pathway can do, i.e., can the two subpopulations of a cancer patient cohort partitioned by some threshold pathway score improve patient prognosis when extended targets are used? Figure This time we processed ENCODE ChIP-Seq binding profiles of GATA3 to identify 355 target genes . These genes are input to the BioTarget pipeline five times, one for each of the five cancer cohorts. An absolute thresholding scheme was applied, i.e., −0.3/0.3 for negative/positive correlation between TF and its target gene, for target derivation. Target genes of TF GATA3 are reported in Table Figure 14. In this analysis, STAD and COAD cohorts are chosen because they show distinct effects of Th1 and Th2 responses on patient survival. In STAD, the Th2 association with a poorer prognosis is seen most clearly at Stage 2, which tends to gradually diminish as the cancer progresses16. A similar observation is made with COAD patients; Th1 and Th2 dominant colon cancers exhibit poorer prognoses at Stage 2, after which this trend becomes less clear. This analysis shows the importance of cancer stage in determining the possible impact of Th1 and Th2 responses in patient stratification.Figure 17. The BCL6 pathway is shown in Fig. et al.18 and its downstream portion including target genes such as STAT, GATA, CCR, NFKB1 and so on from Hatzi et al.19. Figure BCL6 is a transcriptional repressor being considered as a critical regulator of germinal centers where B cells are selected on production of high affinity antibodies6. In Th1 cell differentiation pathway, which has been extended with Up/Down targets, a decoy pathway is created by replacing each gene in the pathway with a randomly selected one while the relationships established in the pathway are kept identical. A total of ten random decoy pathways were created to test against the tailored pathway for Th1 Cell Differentiation (Th1*). We used statistical tests with different criteria, which include TTest, Wilcoxon ranked sum test for difference and correlation. The outcome of this significance test is summarized in Table BioTarget is a pipelined bioinformatics system that allows scientists to identify a small number of testable Up/Down target genes of a pathway including key regulatory TFs. Here we focus on immune response pathways, specifically Th1 and Th2 differentiation, in which roles of TBX21 and GATA3 are firmly established. Generating the small number of target genes is like distilling or enriching from raw materials (ENCODE ChIP targets in our case identified for TBX21 and GATA3) into a small number which demonstrate a strong correlation with the upstream signals known to direct the differentiation pathways. One way to demonstrate the significance of our derived gene sets is to calculate the statistical significance of output genes. We carried out a decoy pathway analysis, an approach which is similar to what has been done in PARADIGMAfter identification of Th1/Th2 target genes using BioTarget, one question concerns how many genes are common to all cancer types, and how many are cancer-type specific. Figure et al.20, expression level of ILF2 is up-regulated in non-small cell lung cancer (NSCLC), and knockdown of ILF2 inhibits the cell proliferation and cell-cycle progression. Another example is LAX1, which is known to be an essential immune-relevant gene encoding membrane-associated adaptor protein on B and T lymphocytes. In Table et al.21 showed that LAX1 can control Ras-MAPK activation, Ca++ flux and nuclear factor of activated T cells (NFAT) activation.As shown in Table et al.22. In Table et al.23. Although leukocyte adhesion and migration are closely-related to humoral immune response, function of ITGAL in cancer immune-oncology remains unclear. Here in Table BioTarget is also capable of identifying target genes whose role in cancer or the immune response has not been previously identified. For example, SLC35B2 gene encodes solute carrier transporter protein that transports sulfate from cytosol to Golgi and is considered an important step in the syntheses of glycoprotein and glycolipid as reported by Kamiyama 24, CD247 encodes the T-cell receptor zeta, which responds to the T-cell mediated type 1 cytotoxic immune signaling by stimulating expression of TBX21 and suppressing expression of GATA3. As shown in Table et al.25, SMAP2 gene encodes stromal membrane-associated GTPase-activating protein 2, which is known to activate GTPase and interact with clathrin. Although specific functions of SMAP2 in cancer or immune are yet to be known, BioTarget seems to suggest that the altered expression of SMAP2 gene in GATA3 may mediate Th2 differentiation pathway in a cancer type specific manner, that is, through up-regulation in LUSC but through down-regulation in STAD and BRCA.For the GATA3-mediated Th2 differentiation pathway, similar argument can be made that BioTarget can determine Up/Down target genes of this pathway. According to Klinke 2014et al.26, GeneMania by Warde et al.27 are powerful but do not address the data reduction issue.The goal of this part is to demonstrate how to incorporate heuristic method into a visualization tool to control pathway components. Each pathway could combine multiple pathways and can exponentially grow unless the complexity of the network is controlled. The existing web-based interactive gene network programs such as PCViz by Cerami In Figs 28. ENCODE peaks are published with IDR, which could be used as a cutoff to include only a small subset of the identified peaks for downstream analysis. The first step of this downstream analysis is using the open source Bioconductor package ChIPpeakAnno which maps ChIP-seq peaks into candidate target genes of the concerned transcription factor . We used human assembly reference GRCh38 for gene annotation and we opted to use “2Kb” distance from binding site to a gene’s transcription start site (TSS). The choice of “2Kb” can be modified to a smaller or bigger number depending on the intent to produce a larger or smaller size of what we call “Potential Direct Target Genes (PDTG)” as the input for Part B as shown in Fig. 28, for example, if the cell line used for the peak generation is from GM12878 immortalized B-Lymphocyte, MCF-7 breast cancer, HepG2 human liver cancer, or K562 immortalized myelogenous leukemia. We show the outcome of our assessment on how much overlap between Conservative and Optimal IDR gene sets is observed when our PDTG generation method is applied to six transcription factors: TBX21, GATA3, BCL6, IRF5, PAX5, and STAT1 in Supplementary Information. These transcription factors are known for their key roles in immune cell development30. Significant overlap was observed in some cases but not in all cases, suggesting the need of careful consideration during PDTG generation. Lastly, as shown in Fig. The overview of the BioTarget data processing pipeline is outlined in Fig. f, let its true downstream be denoted by T(f). T(f) may vary depending on the context  and finding T(f) is known non-trivial . Our strategy is using a route-based scoring scheme which transforms the degree of overly activated or suppressed pathway status into scores9 and uses the scores to compute how likely a member of PTG should be included in T(f). Our scoring scheme is made up of two parts, namely, route propagation score (R) and effect score (M) as shown in Fig. Next is to “distill” PTG to produce a far smaller subset of genes which are more likely legitimate targets of the concerned pathway. Given a transcription factor f, while M estimates the activation/suppression strength of the pathway toward the biological process the pathway is defined for. Propagation score of a route j in a pathway P denoted by rj is a route (1 ≤ j ≤ nj) where nj is the number of routes in the pathway P; gi and gt are a gene and a transcription factor in the route rj, respectively; ng is the number of genes in the route; I(·) is an indicator function of which its value is 1 if the condition meets or 0 otherwise; v is the consistency value of the gene gi in the route rj with respect to the transcription factor gt. Let the gene gi activate gt, then pij is the p-value assigned to gi in the route rj and tpr. The p-value of gi in rj is calculated by the area beyond the observed data point of the probability density function (PDF) of the null hypothesis (h(s) is the PDF of the gene values of the population). Here gi and gt are log2 ratio values and they can have positive values when the test value is greater than the control value, or negative values when the test value is less than the control value. If gi and gt have the same sign, v = 1, and if the signs are opposite, v = −1. In the case that gi inhibits gt, the v = −1 if the signs of gi and the gt are the same, or v = 1. In Equation i in route j, TF expression value, while th gt.R signifies the strength of a particular route(s) potentially responsible for the transcriptome level change of mentclass2pt{minimH0 (population)32. It is calculated as the total area of the PDF of H0 beyond the observed data point. If Y is a random variable of observation and y is an actual observed data point, p-value is Pr(Y ≥ y|H0), Pr(Y ≤ y|H0), or 2 × min(Pr(Y ≥ y|H0), Pr(Y ≤ y|H0)) in the cases of up-regulated genes, down-regulated genes, up/down regulated genes, respectively.The p-value is a probability that the observed data point (sample) is how different from the data set of null hypothesis P denoted by MP is defined as followsEffect score of a pathway gi, BP, while BP. BP is estimated by the signal of TF. nm is the number of target genes in downstream. tpm. A route is traversing from BP backward to TF through up-regulated genes and down-regulated genes of downstream targets. Both scores of MP are leveraging biological process evaluation. When Ak is the number of consistent and significant genes with p-values less than threshold, Ak × (1 − FDR) will be a number of truly consistent and significant genes. Effect score MP of a patient Pk is calculated as n and m are the number of down-regulated genes, and up-regulated genes, respectively.Similar to mentclass2pt{minimC of the subjects whose transcriptome patterns can be considered conveying some strong regulatory signals in one way or the other for the pathway P under consideration. Those demonstrating strong activation signals are grouped into the sub-cohort, say Cu, and those demonstrating strong suppression signals are grouped into the sub-cohort, say Cd. The R score computed for each subject of the cohort C and some preset threshold values are used to make the membership decision. But the FDR introduced in Equation Cu and Cd. The second step is to use transcriptome patterns included in Cu and Cd as references in deciding if a gene gi in PTG should be classified as an Up or Down target of the transcription factor gt in P. For this step Pearson correlation coefficients are calculated between gt and gi for the transcriptome patterns included in Cu. and Cd.. Some preset threshold values for the Pearson correlation coefficient are used to determine if gi. should be included in the Up target group for, say Gu or the Down target group, say Gd. In addition the FDR introduced in Equation Gu and Gd. The pathway is extended with two types of target genes, the Up targets in Gu. and the Down targets ingtGd. Below in Table Choosing Up and Down target genes is achieved in two steps. The first step is to identify two sub-cohorts of the original cohort MP scores of the pathway P is given below.The detailed procedure to “distill” target genes based on P, cohort G.Input: Pathway P′, possible target gene set G′ for the context defined by the cohort C.Compute R score: Calculate mentclass2pt{minimChoose two sub-cohorts and combine them: From the values of Cq, 1 ≤ q ≤ nC, determine the two sub-cohorts Cu and Cd as follows.Cu and Cd include only the subjects whose tcu and tcd, respectively. Both are combined into C′, called the selected cohort.Compute Pearson correlation coefficients: Calculate Pearson correlation coefficients (gt in the pathway P and each candidate target gene (gi) using the selected cohort C′ (G) can be collected from the literature or ChIP-seq experiments.nC′ is the number of the selected cohort C′. gi and gt of the selected cohort C′, respectively. With 1 ≤ k ≤ nC′, gi,k and gt,k are, respectively, candidate target gene gi and transcription factor gt appearing in the k-th patient transcriptome data set included in the selected cohort C′.Select two subsets from potential target genes: Select the subset Gu of G such that Gd of G such that Update the Pathway: Update pathway P into P′ with Gu and Gd.Compute M Scores: Calculate MP′ scores using Equation Output: Extended pathway Gu and Gd, as TF targets in the pathway improves p-value obtainable from the KM survival analysis in a way the functional role of the concerned pathway can be meaningfully plained. The first step is to partition the cohort C into several sub-cohorts using the calculated MP′ scores. For example, for a binary sub-grouping, some threshold value is used to partition the cohort C into one for having high MP′ scores and one for having low MP′ scores and then use them to calculate KM survival rate and p-value and visualize the outcome. Another meth for the evaluation is calculating two sets of correlation between R scores and M scores  and compare them. The correlation coefficient obtained from C′ should be bigger than the one obtained from C., as illustrated in Table Once the pathway extension procedure is completed with some choices of parameters , an evaluation is performed. One particular evaluation method available when dealing with cancer data sets is the KM survival estimate method. We test if the addition of two types, BioTarget is a new tool that uses “similar kind” transcriptome datasets together with ChIP-seq data to extend existing curated signaling pathways by incorporating TF activities. To demonstrate such pathway extension is possible, we took advantage of the publicly available TCGA datasets of different cancer types and the community-curated ENCODE ChIP-seq data. We treat each cancer type data  to form a context, and examine if and how known pathways can be extended. To demonstrate the feasibility of this approach, we used the BioTarget to quantify the activities of key T cell transcription factors associated with Th1 and Th2 cells. Interestingly, we found that different sets of TF downstream targets are discovered for different cancer types, together with a number of common target genes. We were able to use the extended pathways to stratify cancer patients into risk categories. Patient stratification by this method showed a number of cancer-type specific effects of the immune response, with a notably poor prognosis for patients with high Th2 scores for stomach breast and colon cancers. Using this tool, we also discovered that cancers scoring higher for BCL6 activity, a transcription factor expressed by T-fh cells, have a significantly better prognosis. Our data support the usefulness of the BioTarget tool for evaluating/scoring signaling pathway activity, for identifying context-specific biomarkers associated with cell signaling pathways, and for discovering TF target genes that underlie the changes in phenotype associated with these pathways.BioTarget is a small step toward the new opportunity to extend existing signaling pathways with newly available data. There are many issues to be resolved. For example, the current implementation of BioTarget does not handle co-regulation by multiple co-factor TFs. TFs also act on non-coding regions, and how to incorporate TF regulation on non-coding regions poses an exciting challenge. We hope that this work lays the groundwork for a new way to extend pathways by systematically mining datasets with a context.Supplementary info


Polycystic ovary syndrome (PCOS) significantly affects women’s health and well-being. To explore the pharmacological basis of the Erxian decoction (EXD) action in PCOS therapy, a network interaction analysis was conducted at the molecular level.The active elements of EXD were identified according to the oral bioavailability and drug-likeness filters from three databases: traditional Chinese medicine system pharmacology analysis platform, TCM@taiwan and TCMID, and their potential targets were also identified. Genes associated with PCOS and established protein–protein interaction networks were mined from the NCBI database. Finally, significant pathways and functions of these networks were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to determine the mechanism of action of EXD.Seventy active compounds were obtained from 981 ingredients present in the EXD decoction, corresponding to 247 targets. In addition, 262 genes were found to be closely related with PCOS, of which 50 overlapped with EXD and were thus considered therapeutically relevant. Pathway enrichment analysis identified PI3k-Akt, insulin resistance, Toll-like receptor, MAPK and AGE-RAGE from a total of 15 significant pathways in PCOS and its treatment.EXD can effectively improve the symptoms of PCOS and our systemic pharmacological analysis lays the experimental foundation for further clinical applications of EXD. Polycystic ovary syndrome (PCOS) affects 5–20% of all reproductive aged women around the world, and is characterized by hyper-androgenism, infertility, irregular menstrual cycle and polycystic ovarian morphology (PCOM) due to abnormal production of androgens by the ovaries . In addiEpimedium brevicornum (Yinyanghuo), Curculigo orchioides (Xianmao), Morinda officinalis (Bajitian), Angelica sinensis (Danggui), Anemarrhena asphodeloides (Zhimu) and Phellodendron chinense (Huangbo), and is used to mitigate menopausal side effects [Traditional Chinese medicine (TCM) has been continually practiced since 2000 years. The Erxian decoction (EXD) consists of six herbs:  effects , osteopo effects , and ova effects . One stu effects . Another effects . Althoug effects . Recentl effects . Since T effects . Network effects , 17. In http://lsp.nwu.edu.cn/tcmsp.php) is a Chinese medicine pharmacology database containing information about the herbs used in TCM, and absorption, distribution, metabolism and excretion (ADME) characteristics of the individual compounds, their targets, related diseases, and pathways. The database search for EXD revealed its constituent herbs . In drug research and development, approximately 90% of the novel candidates fail the tests due to unexpected toxicity, poor absorption or bioavailability , or poor efficacy [TCMSP   and cytohttp://www.uniprot.org) [http://stitch.embl.de/) [An essential step following the discovery of active molecules is to identify their molecular targets that trigger the biological effects . Bioinforot.org) . Random rot.org) , 28. A crot.org) . Target mbl.de/) . The relmbl.de/) . The nodmbl.de/) .http://www.ncbi.nlm.nih.gov/quality) [Homo sapiens. The CT network was then mapped to the PCOS-related gene network to establish a PCOS-drug interaction network based on overlapping genes, to determine the mechanism of EXD action in PCOS. In such a network, a node can represent a herb, a compound, or a gene/protein, and an “edge” is an association between the nodes. The “degree” of a hub is the number of edges associated with it, and the “betweenness” of a hub is the number of closest associations. The nodes whose connectivity was greater than twice the median of all nodes are selected as the hub nodes in the network. The hubs with high centrality are considered the key hubs in a network.Genes related to PCOS were downloaded from the NCBI Gene database  . The dathttp://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes  pathway analysis were used to analyze the primary pharmacological units. GO is a database that functionally annotates genes and proteins into three main terms—cellular components (CC), molecular functions (MF), and biological processes (BP) [Gene Ontology   and pathses (BP) . The molses (BP) .We obtained 981 active compounds from the initial search of three databases: 649 from TCMSP, 142 from TCM@taiwan, and 455 from TCMID. There were 123 common compounds between the TCMSP and TCMID databases, while TCM@taiwan and TCMID had 142 common compounds amongst them, and 70 compounds were shared across the three databases  network was first built Fig. . In suchBiological functions of the PCOS specific drug targets were annotated to clarify the mechanism of action of EXD in PCOS. GO enrichment analysis was performed on the 50 targets using ClueGO, and the top five BP terms were extrinsic apoptotic signaling pathway, positive regulation of reactive oxygen species metabolic process, protein kinase B signaling, positive regulation of sequence-specific DNA binding transcription factor activity, and response to corticosteroids. The top five MF terms were growth factor receptor binding, cytokine activity, ion channel regulator activity, nitric-oxide synthase regulator activity and steroid binding, while the main CC terms were plasma membrane raft, nuclear transcription factor complex, caveola RNA polymerase II transcription factor complex, platelet alpha granule and platelet alpha granule lumen Fig. . The sigTraditional Chinese medicine (TCM) consists of complex formulations which have hitherto been difficult to characterize, thus limiting their widespread clinical use. In this study, we combined systems pharmacology (SP), pharmacokinetics (PK) and bioinformatics to identify the individual compounds of the EXD formula and their specific PCOS-related targets. EXD is a formulation of six herbs with known ameliorative effects in gynecopathy.After screening EXD for OB and DL, 70 compounds were obtained, of which the most effective were C28  and C13 . Two constituent herbs of EXD contain these compounds. Quercetin, a polyphenol derived from many plants species, is known for its anti-carcinogenic, anti-inflammatory and antiviral activities, in addition to its active roles in platelet aggregation, lipid peroxidation and capillary permeability , cellulaThe drug target network and the PCOS network had 50 overlapping genes, of which 37 were enriched in 15 pathways, and are likely the key genes involved in PCOS treatment. The significant pathways involving the candidate compounds targeting PCOS can be classified as prototypes, direct and indirect pathways depending on their functions. Based on the network analysis, we obtained three proteins of interest—AKT1, IL6 and INSR. In the hyper-androgenic PCOS patients, high levels of AKT1 have been associated with GCs dysfunction . In addiSystems pharmacology and genomics were combined to identify the PCOS relevant targets of the EXD formula. Our findings indicate that 6 of the constituent herbs in EXD act synergistically on certain putative PCOS targets. This study presents a high throughput and economic method of identifying drug targets, and may have significant clinical utility.


The quantity of metastases lesions is an important reference when it comes to making a more informed treatment decision for patients with colorectal cancer liver metastases. However, the molecular alterations in patients with different numbers of lesions have not been systematically studied.We investigated somatic alterations and microsatellite instability (MSI) of liver metastases from patients with single, multiple or diffuse metastasis lesions. A new algorithm “Pathway Damage Score” was developed to comprehensively assess the functional impact of somatic alterations at the pathway level. Pathogenic pathways of different metastasis were identified and their prognosis effects were evaluated. Furthermore, the subnetworks and affected phenotypes of the altered genes in each pathogenic pathway were analyzed.P < 0.001). Further pathway subnetwork analysis showed that except conventional drivers, other genes could also contribute to metastasis formation.Somatic alterations and altered genes occurred sporadically as well as in MSI state in different metastasis types, although MSS patients had more metastatic lesions than that of the MSI patients. Every metastasis group has their own pathogenic pathways and damaged “Cargo recognition for clathrin‐mediated endocytosis” is significantly associated with poor prognosis (Progression of liver metastasis could be driven by the coefficient of all altered genes belonging to the pathways. Thus, compared to somatic alterations and genes, pathway level analysis is more reasonable for functional interpretations of molecular alterations in clinical samples. CRC liver metastasis (CRCLM) is the most common type of CRC metastasis.ERK2 not ERK1 can provide colon cancer cells with the ability to seed and colonize the liver.LUCAT1 by impairing cancer cell invasion.Molecular characteristics are very useful for the prognosis of CRC.In this study, we investigated the genome‐wide exonic variants and microsatellite characteristics of metastatic tumor in CRCLM patients with different number of metastatic lesions using whole exome sequencing. To study the holistic effect of all coding alterations rather than only on traditional cancer genes, a new algorithm named “Pathway Damage Score” (PDS) which could evaluate the accumulative effect of all alterations on tumorigenesis was developed. Additionally, alteration effects on pathway level calculated using algorithm based on all intragene alterations were used to explore the molecular dynamics and indicators for clinical features.22.1A total of 15 patients diagnosed with single lesion (defined as type I) or multiple lesions  or diffused lesions  of CRCLM who had received surgeries between 2016 and 2017 without prior treatment including chemotherapy or radiotherapy at LIAO NING CANCER HOSPITAL & INSTITUTE were enrolled . TNM stage of patients ranged from III to IV according to AJCC 7th edition.Collection and use of all specimens in this study were approved by the Large‐scale Data Analysis Center of Cancer Precision Medicine, Liaoning Provincial Cancer Hospital & Institute . The study protocol was approved by the ethics committee of the Liaoning Provincial Cancer Hospital & Institute  and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants.2.2Genomic DNA was extracted with QIAamp DNeasy Blood & Tissue Kit (Qiagen) from frozen tissue samples. Up to 3 µg of genomic DNA was fragmented into a base‐pair peak of 250‐300 bp fragments. Adaptor‐ligated templates were purified using Agencourt AMPure XP beads, and fragments with an insert size of ~250 bp were isolated. In‐solution exome capture was carried out using the SureSelect Human All Exon V6 kit (60 Mb) (Agilent Technologies). Paired‐end sequencing, resulting in sequences of 150 bases from each end of the fragments, was performed on the HiSeq4000 platform following the manufacturer's instructions .2.3Low quality reads and adapter sequence were removed using TrimmomaticWe used MSMuTect2.4We proposed that pathway abnormality is a key factor for tumorigenesis. The degree of pathway damage depends on the effects of alterations on genes and its interaction pattern in the pathway. The effects of an alteration on a transcript were determined by confidence of alteration detection (defined as MC), degree of dysfunction (defined as ME), and enhancement or restraint of gene function that can be interpreted as effect direction (defined as MED). So, the effect score of a alteration on a transcript (TMES) can be calculated as:The valuation of MC, ME and MED can be learnt from supplementary methods.k is the effect score of an alteration on transcript k, k = 1, 2, ..., m, m is the number of effected transcripts of the gene.A genomic alteration usually can affect several transcripts of a gene at the same time, but we did not know which transcripts were the major components in the cell. Therefore, we used the average TMES of all effected transcripts of a gene to score the effect of an alteration on the gene.j,j = 1, 2, …, n is the importance weight of the gene(j) in the pathway, GDSj is the gene effect score calculated from its somatic variants. Merging mathematic formulas (1), (2), (3), we can form an integrated calculation formula as follows:To evaluate the significance of a certain gene in a pathway, we calculated the number of interacted genes of this certain gene in this pathway to represent the gene's importance weight (GIW) in this pathway. The Pathway, gene‐gene interaction and gene‐pathway information were from the PathwayCommons database.We use formula (4) to calculate PDS of every pathway in every sample Figure.To estimate what degree of PDS of pathways can be pathogenic for tumor progression, we use PDS calculated by known pathogenic alterations from cancer pathogenic database HGMD,2.5For subnetworks, all mutated genes and their direct interacting genes which were involved in cancer related biology processes such as cell proliferation, migration, genomes instability, cell death, angiogenesis, immune response and inflammatory response, were defined as nodes and interactions defined as edges. The networks were plotted using SVG program.2.6P < 0.01 was considered significant.The difference in quantity of metastasis lesions between MSS and MSI‐L samples were evaluated with the Wilcoxon paired test. We used somatic alterations (SNV and CNVs) and overall survival information (from pathologic diagnosis to last follow‐up) from 416 colon cancer patients registered in the project “The Cancer Genome Atlas” (TCGA) to evaluate the prognosis predictor of pathogenic pathways . Kaplan‐Meier survival function was calculated and compared with the log‐rank test. GraphPad Prism 5 software was used for all statistical analyses and 33.1In total, 453 silent and 1904 non‐silent somatic mutations were identified, corresponding to 127 nonsilent mutations per tumor (range of 38‐311) or 2.54 (0.76‐6.22) per mega base Table . NonsileAPC (67%), TP53 (53%) and KRAS (27%) . This helped us to investigate the pathogenic mechanism at a more systemic level of the pathway rather than at the gene level or the alteration level. Compared to RP‐PDS, Gaussian distribution of PDS of pathogenic pathways had a larger variance in our study. This suggests that both conventional and non‐conventional pathogenic alterations can be contributions of pathogenic PDSs. Additionally, PDS distribution of all pathways in our study revealed that nonpathogenic pathways carried lower PDS than pathogenic pathways Figure A. This tWe investigated the pathogenic pathways related to quantity of metastatic lesions. Nine common pathogenic pathways were identified in CRCLMs. The angiogenesis pathway damaged pathogenically in all CRCLM patients indicated that blood vessels are the common or probably the only way for tumor cells to migrate from the colon to liver. Wnt, TGF beta receptor and antigen processing which are closely related to colon cancer progression and immune response were also identified as pathogenic in almost all patients. Integrin signaling and cell surface interactions in the vascular wall pathway which influence the interactions between cell surface receptors and the extracellular matrix (ECM) or the actin cytoskeleton were damaged in 87% (13/15) and 73% (11/15) patients, respectively. These results indicated that these pathways may maintain common phenotypes of tumor cells such as proliferation and colon‐liver metastases. Other common pathogenic pathways like the PPAR alpha‐regulated lipid metabolism could be involved in the tumor microenvironment Figure B,4C.Importantly, we identified special pathogenic pathways for different CRCLM groups using the PDS algorithm. Single lesion: “RUNX1 regulates genes involved in megakaryocyte differentiation and platelet function”, “Nuclear signaling by ERBB4” and “GRB2/SHC events in ERBB2 signaling”; multiple lesions: “SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription and RUNX3 regulates WNT signaling”; diffused lesion: “Cargo recognition for clathrin‐mediated endocytosis” (Figure 3.3To explore how specific pathogenic pathways affect cell phenotypes, and thus contribute to different metastases, the mutated subnetworks of these pathways were studied. KMT2C which mainly induces H3K4me1 at gene enhances, its coding gene mutated recurrently in cancers.4Metastases lesions are an important reference in both surgery decision and prognosis prediction during CRCLM treatment. However, the molecular mechanism of formation and classified methods of single, multiple and diffuse CRCLM are still unknown. Alterations on gene level and MSI features were sporadic across patients, thus can not reveal mechanism of the formation of different quantity of metastasis. But when we transferred alteration affects onto the pathway level by PDS algorithm, the common pathogenic pathways for colon‐liver metastasis and lesion quantity‐special pathogenic pathways were successfully identified. So, evaluating the affect of alterations on cell phenotypes on pathway level is more accurate and systematic than that only on alteration itself or gene level.Treatment benefits could also be obtained from assessing alteration effect on the pathways. On the one hand, type special pathogenic pathways are potential candidate molecular markers for predicting quantity of metastasis lesion formation. Damaged “Cargo recognition for clathrin‐mediated endocytosis” pathway can predict poor prognosis. Additionally, relatively smaller gene sets involved in particular pathways make our method more economical and stabilized in clinical application, which encourages us to develop it in large‐scale samples studies in the future. On the other hand, pathogenic pathways have some implications for therapeutic process. For example, “Clathrin‐mediated endocytosis” is important for endocytosis in the eukaryote cell. It can regulate cell signaling transduction by quickly removing or reducing tyrosine kinase receptor and G protein‐coupled receptors, as well as the activity of synapses and of transporters,Although more accurate and comprehensive understanding of the effects of tumor alteration are acquired at the pathway level, tumor cell phenotypes were usually determined with various genes and pathways.Xue, Zhao, and Ma are employees of Wankangyuan Tianjin Gene Technology, Inc No other disclosures were reported.ZZ and XY performed the experiments. FM and SZ worked on bioinformatics analysis. ZZ and FM wrote the manuscript with the help from all the authors. H. P., JG and LL conceived the experiment. HP, ZZ and JG supervised the project. All authors read and approved the final manuscript. Click here for additional data file.


U. prolifera that is the main species leading to green tide, a harmful plague of floating green algae.Carotenoids are widely distributed in plants and algae, and their biosynthesis has attracted widespread interest. Carotenoid-related research has mostly focused on model species, and there is a lack of data on the carotenoid biosynthetic pathway in U. prolifera samples, that is the main species leading to green tide, a harmful plague of floating green algae at different temperatures revealed that its terpenoid was highest in the samples subjected to high temperature at 28 °C (H), followed by the samples subjected to low temperature at 12 °C (L). Its terpenoid was lowest in the samples subjected to medium temperature at 20 °C (M). We conducted transcriptome sequencing  of samples that were subjected to different temperatures to study the carotenoid biosynthesis of U. prolifera. There were 1125–3164 significant differentially expressed genes between L, M and H incubation temperatures, of which 11–672 genes were upregulated and 453–3102 genes were downregulated. A total of 3164 genes were significantly differentially expressed between H and M, of which 62 genes were upregulated and 3102 genes were downregulated. A total of 2669 significant differentially expressed genes were observed between L and H, of which 11 genes were upregulated and 2658 genes were downregulated. A total of 13 genes were identified to be involved in carotenoid biosynthesis in U. prolifera, and the expression levels of the majority were highest at H and lowest at M of incubation temperature. Both the carotenoid concentrations and the expression of the analysed genes were lowest in the normal temperature group, while low temperature and high temperature seemed to activate the biosynthesis of carotenoids in U. prolifera.The carotenoid content of U. prolifera.In this study, transcriptome sequencing provided critical information for understanding the accumulation of carotenoids and will serve as an important reference for the study of other metabolic pathways in The online version of this article (10.1186/s12864-018-5337-y) contains supplementary material, which is available to authorized users. Terpenoids are the most widely distributed natural compounds in nature, playing an important role in growth, development, light absorption, hormone synthesis, photoprotection and stress resistance in plants . TerpenoCarotenoids are mostly C40 compounds consisting of eight isopentenyl-pyrophosphate units and comprise a class of natural pigments that belong to the tetraterpenoid group . CarotenUlva prolifera , that is the main species leading to green tide, a harmful plague of floating green algae [U. prolifera blooms have occurred in the southern Yellow Sea continuously and led to a green tide disaster along the coast of the Shandong and Jiangsu provinces of China. This phenomenon had negative impacts on the local economy and environment [U. prolifera is an increasing focus of scientists and has drawn considerable attention from the Chinese government [en algae . The macen algae . These fironment –13. Thervernment , 15.With the development of sequencing technology, transcriptome sequencing has been widely used in the study of various species. Transcriptomics revealed dynamics of photopigment , flavonoU. prolifera under different temperatures. All of the genes involved in the MEP pathway and the downstream pathway were screened, and their expression patterns were analysed through transcription data. Our results not only provide insight into carotenoid biosynthesis in U. prolifera but also provide an important reference for further study in U. prolifera. Terpenoids metabolism is an important metabolism of U. prolifera, we made a significative preliminary work for exploring the reason of the rapid formation of green tide in terms of the metabolic activities in the future.Temperature is a key factor for terpenoid biosynthesis in algae . In thisThe quantitative determination of carotenoid, Chl a and Chl b content was performed using a spectrophotometer. The concentrations of carotenoids, Chl a and Chl b in the L group were 3.34 ± 0.03 μg/ml, 2.20 ± 0.07 μg/ml and 1.67 ± 0.01 μg/ml, respectively. The concentrations of carotenoids, Chl a and Chl b in the M group were 2.80 ± 0.03 μg/ml, 1.78 ± 0.05 μg/ml and 1.34 ± 0.01 μg/ml, respectively. The concentrations of carotenoids, Chl a and Chl b in the H group were 4.72 ± 0.03 μg/ml, 3.2 ± 0.04 μg/ml and 1.90 ± 0.06 μg/ml, respectively  reads accounted for 96.02–96.14% of the reads . There were matches for 23,625 unigenes (47.56%) in the Nr database, 18,577 unigenes (37.40%) in the Swiss-Prot database, 12,652 unigenes (25.47%) in the KEGG database, 16,046 unigenes (32.30%) in the KOG database, 20,005 unigenes (40.27%) in the eggNOG database, 18,027 unigenes (36.29%) in the GO database, and 38 unigenes (0.08%) in the Pfam database.A total of 12,652 unigenes were annotated in the KEGG database and were involved in 24 metabolic pathways  among L, M and H were compared. There was a high degree of correlation between the qRT-PCR results and the RNA-Seq results Fig.  indicatiU. prolifera and there have been limited attempts to understand carotenoid biosynthesis in this species. In this work, we analysed the carotenoid biosynthetic pathway in U. prolifera by transcriptome sequencing.Carotenoids have been studied for more than 100 years. They play important roles in the structure and function of the photosynthetic apparatus of living organisms, including bacteria, algae and higher plants . More thChlorella, Chlamydomonas, Dunaliella and Haematococcus [5-compound, is the source of chlorophylls and carotenoids. There are two pathways of synthesis of this precursor: the MVA pathway and the MEP pathway [Functionally confirmed enzymes of carotenoid biosynthesis have been found in algae species such as tococcus –42. Isop pathway . The pat pathway –47.U. prolifera, such as AACT and HMGR. The other genes, HMGS, MVK, PMK and MVD, were absent in the transcriptome data. This suggests that a complete MVA pathway to synthesize terpenoids is lacking in U. prolifera. The results were in accordance with the results of studies on Porphyra umbilicalis, Cyanidioschyzon merolae 10D, and Chlorella zofingiensis [In our study, we found that some of the genes of the MVA pathway exist in ngiensis –50. The ngiensis . Some grngiensis , 53.U. perolifera.Carotenoids are derived from the plastid-localized MEP pathway , for whiU. prolifera. This finding revealed that carotenoids are involved in the response to temperature stress; in other words, carotenoids might have a protective function.Secondary metabolites are the result of biological and non-biological interactions between organisms and the environment throughout evolution, and secondary metabolites play a critical role in improving the ability of organisms to survive and coordinate with the environment . The proU. prolifera that were subjected to different temperatures. The results provided a comprehensive explanation of carotenoid biosynthesis in U. prolifera. The MEP pathway was detected from the transcriptome data. However, the MVA pathway was absent in terpenoid metabolism. Temperature is a key environmental factor affecting carotenoid biosynthesis. The production and concentrations of carotenoids were susceptible to temperature, and carotenoid concentrations were upregulated when U. prolifera were subjected to temperature stress. The data reported in this study provide critical information for understanding the accumulation of carotenoids and will serve as an important reference for the study of other metabolic pathways in U. prolifera that is the main species making green tide.In this study, we conducted transcriptomic and carotenoid biosynthetic analysis on samples of U. prolifera samples were collected in March 2018 from Pyropia rafts  in Nantong, Jiangsu, China. The samples were cultivated in seawater medium, and cool-white fluorescent light was provided on a 12:12 L:D cycle. The cultivation environment of the samples was as follows: 120 μmol photons m− 2·s− 1 in seawater with a salinity of 30. There were three temperature regimes: 12 °C was set as the low temperature (L), 28 °C was set as the high temperature (H), and 20 °C was set as the medium temperature (M). The medium temperature group served as a control group. The samples, which were cultivated at the three different temperatures for 7 days, were frozen in liquid nitrogen until they were used for RNA extraction.Samples (0.05 g) from the three different temperatures were weighed, ground into powder, and placed into 5 mL of an 80% acetone solution. The samples were held at 4 °C for 12 h and then centrifuged at 10000 rpm for 20 min, and the supernatants were collected. The absorbances at wavelengths of 470 nm, 646.8 nm and 663.2 nm were measured using a spectrophotometer , and the quantitative determination of carotenoid, Chl a and Chl b levels was performed with the following formulas :470 – 1.82 × Chl a – 85.02 × Chl b)/198Total carotenoids (μg/ml) = (1000 × A663.2 – 2.79 × A648.8Chl a (μg/ml) = 12.25 × A646.8 – 5.1 × A663.2Chl b (μg/ml) = 21.5 × ATotal RNA from all samples was extracted using an E.Z.N.A.® Plant RNA Kit Omega Bio-tek, USA), and an aliquot of total RNA was treated with DNase I  to remove DNA. RNA integrity was measured by 1% agarose gel electrophoresis, and RNA purity was detected with a NanoDrop spectrophotometer .https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi). The data and scripts used for the analysis is available with accession number SRP157932.These RNA samples were reverse transcribed into cDNA using a SMARTer™ PCR cDNA Synthesis Kit , and cDNA libraries were created. After the quality of the cDNA libraries was assessed with an Agilent Bioanalyzer 2100 system , sequencing was conducted using an Illumina HiSeq X Ten sequencer by Shanghai OE Biotech. Co., Ltd. The raw data were uploaded to the NCBI Sequence Read Archive  with sequences in the National Center for Biotechnology Information (NCBI) non-redundant nucleotide sequence (Nt) database (ftp://ftp.ncbi.nih.gov/blast/db). The best dataset was selected.The data were assessed for contamination before subsequent analysis; 250,000 pairs of reads  from the data were extracted randomly, and then the data were aligned using BLAST , the EuKaryotic Orthologous Groups (KOG) database (http://www.ncbi.nlm.nih.gov/COG/), the Gene Ontology (GO) database (http://www.geneontology.org), the Swiss-Prot database (http://www.uniprot.org/), the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) (http://eggnogdb.embl.de/) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/pathway.html). We screened for proteins with the highest sequence similarity for functional annotation information. In addition, the HMMER program [http://pfam.xfam.org/) to screen the protein family with the highest score.Gene function was annotated using the DIAMOND 4.0 program  with an  program  was usedAnalysis of the differential expression of unigenes among different samples was conducted using DESeq , a methoU. prolifera. But most of the genes encoding key enzymes in the MVA pathway were not found, except for AACT and HMGR. In addition, a total of 22 expressed unigenes encoding carotenoid biosynthesis enzymes were found, and six genes encoding key enzymes in carotenoid biosynthesis were identified in U. prolifera. Functional genes that participate in terpenoid biosynthesis in Chlamydomonas reinhardtii and Chlorella variabilis were selected from NCBI and aligned with the sequences of U. prolifera. The phylogenetic analysis was carried out in MEGA 5.1 software [The expression profiles of genes involved in terpenoid biosynthesis were analysed using KEGG pathway annotation. A total of 56 expressed unigenes encoding terpenoid biosynthesis enzymes were found in software  with the-ΔΔCt relative quantitative method. All MEP pathway genes were chosen for analysis of expression patterns in different samples that were subjected to the three different temperatures . The expression levels of these selected genes from qRT-PCR analyses were assessed in comparison to the differentially expressed genes (DEGs) from RNA-Seq.The expression levels of the seven unigenes that participate in the MEP pathway were examined by qRT-PCR. The primers for qRT-PCR are shown in Table Additional file 1:Figure S1. GO annotation of the non-redundant sequences of all the samples. Three primary GO categories and 56 subcategories were summarized in the GO database. (TIF 9821 kb)Additional file 2:Figure S2. GO terms significantly enriched in DEGs in comparisons of L, M and H. (TIF 27551 kb)