Complex disorders often involve dysfunctions in multiple tissue organs. Elucidating the communication among them is important to understanding disease pathophysiology. In this study we integrate multiple tissue gene expression and quantitative trait measurements of an obesity-induced diabetes mouse model, with databases of molecular interaction networks, to construct a cross tissue trait-pathway network. The animals belong to two strains of mice (BTBR or B6), of two obesity status (obese or lean), and at two different ages (4 weeks and 10 weeks). Only 10 week obese BTBR animals are diabetic. The expression data was first utilized to determine the state of every pathway in each tissue, which is subsequently utilized to construct a pathway co-expression network and to define trait-relevant and trait-linking pathways. Among the six tissues profiled, the adipose contains the largest number of trait-linking pathways. Among the eight traits measured, the body weight and plasma insulin level possess the most number of relevant and linking pathways. Topological analysis of the trait-pathway network revealed that the glycolysis/gluconeogenesis pathway in liver and the insulin signaling pathway in muscle are of top importance to the information flow in the network, with the highest degrees and betweenness centralities. Interestingly, pathways related to metabolism and oxidative stress actively interact with many other pathways in all animals, whereas, among the 10 week animals, the inflammation pathways were preferentially interactive in the diabetic ones only. In summary, our method offers a systems approach to delineate disease trait relevant intra- and cross tissue pathway interactions, and provides insights to the molecular basis of the obesity-induced diabetes. Phenotypic traits are properties that emerge from the interactions of genes within a dynamic environmental framework. A major goal of systems biology is to understand how the interactions lead to the observed traits A basic bioinformatics question that arises is, in mapping the genetic architecture of a disease, is it more efficient to develop gene level metrics and make assessment of gene networks through their members' relevance to disease; or to develop network level metrics that directly assess a whole network? In the gene expression data analysis, both approaches were developed, either starting with differentially expressed genes followed by identifying pathways with enhanced presentation among them; or starting with pathways (or predefined gene set) directly through evaluating the expression distribution shift of the whole gene set et al proposed an approach that first defines disease associated genes through literature mining, followed by associating pathways to diseases based on enriched disease gene presence among pathway members, and linking different diseases based on pathway sharing Networking individual genes is known to suffer from high noise and high false positive rate. On the other hand, networking pathways has demonstrated its advantage in providing more relevant biological insights in understanding disease pathogenesis and in establishing the inter- disease relationships Recently, Keller et al profiled gene expression in six tissues , and measured eight quantitative traits  in a mouse model of obesity-induced diabetes In a similar study by Dobrin et al In both studies www.genome.jp/kegg/) as the unit of our operation. We directly characterize the state of each pathway using a new quantitative metric that considers contributions both from individual genes and from their interactions. (2) The approach by Keller et al restricted their analysis to genes that showed one of the 15 predefined patterns Compared to the existing works http://www.ncbi.nlm.nih.gov/geo/, GSE10785). In this study Multi-tissue microarray data were obtained from Gene Expression Omnibus , and a Perl script is written to curate the information of pathway links maintained in KEGG. Functional interactions of mouse genes were downloaded from the Princeton mouseNET (http://avis.princeton.edu/mouseNET/). mouseNET uses probabilistic framework to integrate diverse genetic and genomic data to generate a functional network for the laboratory mouse. It allows the users to predict novel functional assignments and network components 187 mouse pathways were downloaded from KEGG . The scheme of the pipeline is given in We propose a pipeline to construct the network of pathways and traits by integrating the multi-tissue gene expression, trait measurements, and pathways from KEGG  is r>0.7 as the threshold for correlation when constructing networks in each of eight groups of mice, and r>0.4 when constructing shared network for all 40 mice. We have also tried other correlation indices, including the percentage bend correlation index Both the global networks of all animals and the networks specific to each of the eight groups of mice were constructed. In this study we used Spearman correlation coefficient to construct the pathway co-expression network. The threshold values were determined through permutation tests. Briefly, the expression data of different genes were randomly permuted 10,000 times, all pairwise pathway correlation coefficients were recalculated each time. Using the permutation results, the p-value for each correlations value is then determined. We chose p<0.005 as the threshold for significant correlation, this correspond to Fisher's exact test was used to evaluate enhancement in pathway interaction as compared to the KEGG annotation, and multiple testing was corrected using the false discovery rate (FDR) i.e. the fraction of shortest paths between all other nodes that pass through a given node in the network, were determined adopting Ulrik Brandes' algorithm http://pajek.imfm.si/) was used to visualize trait pathway network with partition of the nodes based on pathway category and tissues types.A Java script based on the Cytoscape library was developed to analyze the topological properties of the trait-pathway network, including the network degrees, betweenness, and the distributions of linking pathways and “hub” pathways in different tissues. Edge betweenness, Using all animals, we identified a number of trait-relevant tissue-specific KEGG pathways: 186 for insulin, 32 for glucose, 19 for islet number, 34 for PAI1, 100 for resistin, 107 for TG, 209 for weight, and 9 for adiponectin. The co-expression of KEGG pathways were determined based on their linkage annotated in KEGG and the correlative variations in their PCI. We found that the pathways annotated to be linked by the KEGG database share high PCI correlation both intra and cross tissue. The correlation is significant even after adjusting for the number of shared genes between the different pathways (data not shown). The cross tissue pathway co-expression is likely brought about by the communication between different tissues and suggests the importance of characterizing such crosstalk during disease.http://zen.dom.uab.edu:8080/pathwaynetwork/cytoscape/, where one can view the details of the network with several built-in operations and examine each node and its neighbors. Most of the pathways are metabolism or inflammation related, which is consistent with the current understanding of T2D. To appreciate the general theme of this very complex figure, pathways that are in the same tissue and are from the same KEGG pathway categories were clustered together. The categories include Metabolism, Genetic information processing, Environment information processing, Cellular possesses , and Human diseases. The presence of pathway members from each category is further evaluated using Fisher's exact test. The results for all 30 clusters (5 KEGG categories×6 tissues) are given in Utilizing the trait-relevant and trait-linking pathways and the pathway co-expression network, we constructed the trait-pathway network, which is given in It was previously observed that known disease genes generally exhibit tissue specific activity, with expression levels altered in the tissues where specific gene defects cause pathology We first investigated the global topological properties of the trait-pathway networks in all six tissues. The degree distribution of the network nodes is given in The power-law dependence at degrees >8 is indicative of a small proportion of pathways serving as hub nodes that connect to a large number of other pathways. In the study of gene or protein networks, highly connected nodes are often considered important to network function. For instance, analysis of the yeast protein-protein interaction network revealed that the highly connected genes are more likely to be essential for survival The citric acid cycle (TCA) is series of chemical reactions used by all aerobic organisms to generate energy. It breaks down pyruvates from glycolysis (and other pathways), and generates ATP by oxidative phosphorylation. The importance of the TCA cycle and the pyruvate metabolism pathway in obesity and T2D has been demonstrated Edge betweenness, which measures the fraction of shortest paths going through a node, is an important centrality index In scale-free networks, high degree nodes tend to have high betweenness. In our trait-pathway network, most of the pathways with high betweenness also have high degree  has been found to be a major regulator of body fat stores, energy balance, and insulin sensitivity in vivo. Increased expression of PTP1B is associated with insulin resistance in rodents and humans whereas deletion of PTP1B leads to leanness and insulin sensitivity in rodents 8 out of the 9 pathways in the second cluster are in adipose tissue. MAPKs are intracellular signaling pathways that play a pivotal role in many essential cellular processes such as proliferation and differentiation, specifically in adipocyte differentiation and obesity The trait-pathway network presented in http://zen.dom.uab.edu:8080/pathwaynetwork/cytoscape. The basic network characteristics include the total number of nodes and edges are summarized in Furthermore, we constructed a group-specific network for each of the 8 animal groups in addition to the global network for all animals. The networks in Cytoscape, viewable SIF format files, are given in In more detail, the top overrepresented pathways and their first neighbors in the diabetic BTBR-obese-10 wk group are displayed in Microarray studies have shown that genes involved in oxidative phosphorylation (OXPHOS) exhibit reduced expression levels in the skeletal muscle of T2D and prediabetic subjects. These changes may be mediated by the peroxisome proliferator–activated receptor γ coactivator-1 (PGC1) pathway. Decreased expression of PGC1α- and PGC1β-responsive OXPHOS genes in muscle, and of genes involved in oxidative phosphorylation in pancreatic islets, were observed in T2D patients One critical contributing factor to obesity induced diabetes is the inadequate insulin secretion resulting from β-cell death In this study, we proposed an approach to build tissue-specific, disease trait-pathway networks through associating co-activated pathways, and trait-relevant and trait-linking pathways. Our strategy goes beyond single gene based analysis. It utilizes the PCI to capture the overall activity of each pathway under given experimental conditions, which incorporates contributions from both the individual genes and how they are geometrically situated in the network. The PCI measure is then used to infer interactions between pathways and between T2D traits and pathways. To our knowledge, this is the first of its kind that delineates the disease traits at the genetic pathway level.i.e., those with high degree and high betweenness, are closely involved in glucose metabolism and insulin. A core of pathway clusters was identified that may provide a relatively complete view of the key pathways and their interactions that potentially mediate disease pathophysiology for T2D. In a group-specific analysis, we found a difference in active pathways between the obese animals that developed diabetes and those that did not. While those involved in glucose metabolism and oxidative stress are interactive in both groups, those involved in inflammation exhibit enhanced interaction with other pathways only in the obese animals that develop diabetes. We believe that such multi-scale  systems analysis will provide valuable insight into disease etiology and is essential to better understand the pathophysiology and the pathogenesis of a complex disease like T2D.The new approach revealed a number of findings compared to original analysis by the authors that generated the dataset Obesity induced diabetes is a complex issue; multiple pathways in many tissues are involved. The bioinformatics study presented in this work offers a glimpse of the underlying genetic architecture. There are still many questions to be answered. For instance, in In this study we only analyzed data from two mouse strains. It will be interesting to compare the trait-pathway networks across different models of T2D, which would be valuable to understanding the disease etiology and how well each model represents the human disease. This approach can be generalized from the analysis of multiple traits of one disease to the interrelations among a set of different diseases. Then diseases can be linked through their relevant and linking pathways. The problem of associating multiple diseases is subsequently converted to the problem of network comparison. Finally, it would also be of interest to study trait-pathway networks in general across different species. The results will shed light to the functional evolution of related pathways and pathway interactions.Table S1The tissue-specific pathways relevant to each trait.(XLS)Click here for additional data file.Table S2Network measures, including the total number of nodes and edges, of the trait relevant pathway network in each of weight animal groups.(XLSX)Click here for additional data file.Table S3Pathways with enhanced interactions in each of the eight animal groups.(XLSX)Click here for additional data file.Figure S1Trait-pathway networks in 8 groups in Cytoscape viewable SIF format. (A) B6 lean 4 week. (B) B6 lean10 week. (C) B6 obese 4 week. (D) B6 obese 10 week. (E) BTBR lean 4 week. (F) BTBR lean10 week. (G) BBR obese 4 week. (G) BTBR obese 10 week.(ZIP)Click here for additional data file.


Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe.http://www.geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results.We present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://metnetdb.org/MetNet_BirdsEyeView.htm.BirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from  Rapid technological innovation is enabling new biological approaches, and accelerating biologists' ability to collect large scale transcriptomics, metabolomics and proteomics data. The important trend emphasizing deposition of data and metadata in publicly-available databases means that this omics data is available to all e.g. -4). Extr. Extr4])Because the time it takes to investigate massive data manually and extract useful biological knowledge is often prohibitive, functional genomics tools for data analysis and visualization are critical and many approaches have been or are being developed. Some approaches apply only to single experiments or limited data, whereas others can handle multiple experiments and large biological networks. Histograms and line graphs have been used to show experimental values for many samples at once, e.g. Caryoscope -8. Heat Data analysis can provide powerful views of raw data. However, it reveals less about the overall biological context, such as pathway and cellular information at various granularities. Another approach to visualizing experimental data is to integrate it into a biological network graph. Reactome  shows a Here we introduce a software application to help biologists analyze transcriptomics including RNA-Seq and microarray data. This tool, BirdsEyeView (BEV) links a graphical Cellular Map view, a Pathway Tree view, and a Gene Ontology view together to display experimental results. In addition to incorporating statistical analysis, BEV focuses on the interactive display of experimental entities, in this case transcripts, in different locations of the Cellular Map, in a dynamic Pathway Tree, and using Gene Ontology views based on the each entity's annotation information. As such, it gives biologists a graphical "Birds Eye View" of experimental data, hence its name, "BirdsEyeView."BEV is a part of the MetNet platform, and is designed to visualize biological information and place that information within a biological context. The MetNet  bioinformatics platform containing BEV is a suite of software applications designed for analysis of genomic, proteomic, transcriptomic, and metabolomic experimental data ,1,29. MeAt the center of the MetNet platform is the MetNet database, MetNetDB. MetNetDB includes an integrated metabolic and regulatory network for Arabidopsis based on extant databases and human curation. Network and pathway data for other photosynthetic organisms, , humans, and yeast are in development. The MetNetDB structure integrates biochemical interactions, metabolic pathways, regulatory networks, genome information, Gene Ontology and other relevant annotation, to enable formulation of testable hypotheses about transcriptomic, proteomic, and metabolomic data . The MetBEV directly accesses the information stored in MetNetDB, together with transcriptomics data in a user-uploaded file, to provide users with overviews of their data in the context of pathways, subcellular location and Gene Ontology terms. It provides unique views of experimental results by mapping transcripts to different views based on those entities' annotation in MetNetDB. In addition, it graphically  indicates experimental differences among entities across different compartments in different views. Currently, BEV provides three views: a Cellular Map on a fixed cellular diagram, a Pathway Tree in which views are dynamically displayed as nested rectangles, with different sizes representing different pathways, and Gene Ontology Hierarchy which provides a series of hierarchical rectangles mapped to Gene Ontology classifications Figure . BEV mapA user's choice of visualization and analysis software for transcriptomics data will depend on the expertise and biological interests of that user. BEV incorporates basic statistical analysis, and also allows the user to conduct an appropriate statistical analysis using other software and visualize the results of this analysis graphically. This provides a biological context based mostly on experimentally-based cellular location and pathway information while giving users links to the network information in MetNetDB.Distinctions between BEV and existing software include: 1) Dynamic chunking enables experimental data with detailed annotation to be displayed without occupying huge spaces; 2) BEV provides an interactive overview of the distribution of a whole set of experimental transcriptomic data in the context of cellular, pathway, and Gene Ontology views; and 3) BEV enables a visual comparison of experimental results in which the user can select which combined view of memberships to visualize. As a result, BEV is a quick and easy tool for aiding biologists in hypothesis formulation, and for providing a first look at the data in a biological context.BEV is a stand-alone Java application that accepts user input data and gives users interactive "Birds Eye Views" of their input data by mapping the data to various views. For demonstration purposes, BEV makes it easy to load a default sample dataset from the MetNet Website, so that the program does not require user-generated data files to try the software. This test dataset is from experiments on perturbations of RNA accumulation associated with stress, provided by Mittler .BEV reads a list of biological entity IDs in the input file and finds the corresponding biological annotations by queries to the MetNetDB database including gene name, location, pathway, and Gene Ontology information. The information in MetNetDB is regularly updated from different databases and analyses. Based on the annotation information, the program displays the RNAs in the input file as small graphical icons (circles or rectangles) in different places in the different views. The program adjusts icon colors based on the average expression value of the entity represented by that icon. As such, patterns of experimental input will be shown in different views so that users can view the distribution of highly accumulated RNAs across compartments, pathways, and Gene Ontology Processes. Users can switch and combine views and experiments to see different views of the data. The user can view individual samples, or compare data from any two samples in the input data file.BEV can display experimental results, such as transcriptomic analyses. For either RNA-Seq or microarray analysis, data can be input as a single, combined file or as separate files. For RNA-Seq data, the input files are a file mapping values to gene IDs/locus IDs, a file containing a list of sample, and a file of numbers describing the values of experimental results. For microarray results, files are a chip list file containing a list of Affymetrix probe IDs (chip probes) in the experiments, a file holding the list of samples, and a file of numerical values of experimental results.p-values below a certain threshold retained.If the user processes data by subjecting it to statistical screening before submitting as input, BEV could be used to analyse a data set containing only genes whose data meets a desired quality threshold. For example, statistical analysis could be applied to identify genes that are differentially expressed relative to a control, and only genes with The main interface is divided into four panels Figure : an infoThe view panel is the largest, and holds different views . The proteins encoded by the transcripts specified in the user's input file are represented as icons inside each view.The legend panel contains a legend and user options for customizing view panel. User options include changing color range values, and user-selected filters for biomolecular entities whose data values are over or under the selected values. In addition, users can select the maximum number of entities to be represented per icon, and the maximum number of entities to show in the annotation panel before opening a new frame. The number of entities per icon has a view scope based minimum value based on the number of entities in each cell compartment and the sizes of the compartments, such that all compartments can display all entities that they contain.The annotation panel at the bottom of the screen shows detailed information about entities selected by the user from the view panel icons.In this view, an image showing the structure of a plant cell is displayed. See Figure After loading the input files, the program displays entities from the list of input files in appropriate compartments with graphical icons. Entity icons are placed in compartments based on the location information about each entity loaded from MetNetDB. The locations represented in the cellular map view indicate the known or suspected location within the cell of the protein corresponding to the transcript. Entities whose location information is not known are not displayed. If there are possible multiple locations for an entity, then the entity will be shown in multiple compartments.http://casoilresource.lawr.ucdavis.edu/drupal/node/192, [p-values, etc.). The default is that higher values are assigned darker colors. The program automatically adjusts the color distribution based on the current value range. Therefore, users can more easily identify differences between low and high values. In addition, users can adjust the color range to explore entity value distributions over a narrower or wider value range. In addition, entity icons within each compartment are arranged from low value (light colors) to high value (dark colors). This coloring strategy facilitates users being able to notice potentially interesting distributions of entities across particular compartments and categories.Because an important function of BEV is to display the user's transcriptomics data in a biological context, the icons are color-coded according to the corresponding values in the transcriptome data. The program uses the YIOrRd (light yellow to orange to dark red) color system of the RColorbrewer color combination strategy  to reprBEV uses the size of each compartment space in the cellular map view and the icon size to calculate how many entity icons the view can accommodate. When loading a file that contains a long list of entities, the number of entities may exceed the maximum number of icons that the area of a compartment in the image can accommodate. In this situation, the program will use one icon to represent multiple entities. The numbers of entities per icon that are displayed is determined by the number of entities that need to be represented and the space available in the image of the cell. In this way, BEV can accept and display inputs with any number of entities. A disc shape is used to represent a single entity. When a unit represents several entities, its shape is displayed as rectangular rather than round. This information is noted in the legend panel.http://metnet.vrac.iastate.edu/MetNet_atGeneSearch.htm when the user right-clicks an icon. This displays all information related to this entity stored in MetNetDB.To view detailed information about an icon, that icon can be clicked, and the icon's name, value, location, and pathway will be displayed as a list in the annotation panel. The annotation information of all the selected icons is shown in the annotation panel. Annotation of entities shown in multiple compartments is not repeated even if multiple occurrences of them in different compartment are selected. The annotation display for an icon disappears when the user unselects the icon by clicking it again. A new annotation list window opens when a list has too many entities to fit in the allotted space, and the number of entities displayed in a new window can be controlled by the user in the legend panel. Additional detailed information about each entity is provided via AtGeneSearch The BEV software subdivides the overall Pathway Tree view rectangle. See Figure The pathways and their corresponding cellular compartments are preloaded by the program from its configuration file and MetNetDB, and are independent of the user input file for the transcriptomic experimental data. Currently, pathways are only known for a small proportion of the known genes, so the Pathway Tree view tends to be the sparsest of the three views. To view the relationships between pathways, cellular compartments, and transcriptomics data, we use the Treemap  approachp-values associated with each pathway that indicates how likely it is to for the pathway to contain that number of genes by chance. Each pathway has its p-value shown in its mouse-over text. The foreground of each pathway rectangle is highlighted in red if the pathway has a p-value satisfying a user-configured threshold for flagging as an over-represented pathway. The threshold can be set in the legend panel. The Pathway Tree view thus provides a simple way for a biologist to gain a better understanding of differentially expressed genes in the context of the pathways they participate in.Meanwhile, BEV evaluates overrepresentation of pathways in the data by performing a Fisher exact test for each pathway in which at least one gene from the input list is found. The test is based on the Arabidopsis or the soy genome, depending on which data is input. The Fisher calculation gives the user a list of http://www.geneontology.org; GO) Biological Process and Molecular Function annotations. GO contains three structured controlled vocabularies (ontologies): biological processes, cellular components and molecular functions. The ontologies are structured as directed acyclic graphs. These can be organized as hierarchies, and one entity may be in multiple locations. The Gene Ontology view shows each GO term (category) as a small rectangle within a series of hierarchically nested rectangles. Experimental entities in each Gene Ontology category rectangle are depicted as small square graphical icons  level GO categories as smaller sub-rectangles which hold the associated entity icons. This process can be repeated, subdividing rectangles until the lowest (most specific) GO level is reached. By clicking the name of an open category, or clicking the "up" button, users can delete the sub-rectangles in the current rectangle and thus close the current category display and move to a more general category. The color of the rectangles in different levels of the GO hierarchy differ from each other and from entity icons, so that users can easily see the different levels and the icons.The design of entity icons and access to annotation in the Gene Ontology view is the same as in the Cellular Map view: an icon can represent one or more entities, basic annotation information can be called up in pop-ups with more detail in the annotation panel, and fully detailed annotations can be called up in a new web browser window.BEV supports analysis of experimental transcriptomic data to enable the user to compare data from two samples . For these comparisons across conditions, users can select comparisons of the data by (i) value differences ; (ii) fold changes ; or (iii) difference folds .Currently, visualization in BEV is limited to comparison between two groups of data ; in the future, approaches for overviews of the biological context of multiple samples will be explored. Also, additional statistical analysis can be introduced into BEV to let users extract information from experimental data more conveniently.BEV can combine views, allowing users to track entities across different views. Users may choose this option from the view options box. The multiple view option permits all the same operations on the entity icons as does a single view. Because each view in a two-view display has less screen space than on a single view display, fewer details might be visible. For example, in Figure Here we use experimental data downloaded from PLEXdb, specifically the means of three replicates for all samples in the experiment "Genomics of Soybean Seed Development Using Laser Capture Microdissection"; this experiment was a microarray-based study designed to investigate the differences in expression levels between different compartments and developmental stages of soybean seeds . TranscrBEV was developed as an alpha version in 2009. The software has been improved and expanded in 2012 based on user input. BEV provides cell, pathway, GO Biological Process, and GO Molecular Function views for experimental transcriptomic data. Users can view their transcriptomics data in the context of biological information, and can flexibly focus in on potentially interesting entities, cell compartments, pathways, biological processes and molecular functions relevant to their experimental conditions. These capabilities help support the needs of systems biologists to view transcriptomics data both in broader contexts as well as in considerable detail. Together with statistical tools such as R, Bioconductor , exploRaBEV: BirdsEyeView; MetNetDB: MetNet database; GO: Gene Ontology.The authors declare that they have no competing interests.LZ designed the algorithms and developed the BEV software. DB, DC, and ESW determined the goals, system architecture and usability design. YW and LL contributed to interactions between MetNetDB and BEV interactions. All authors contributed to the manuscript.


The continuing discovery of new types and functions of small non-coding RNAs is suggesting the presence of regulatory mechanisms far more complex than the ones currently used to study and design Gene Regulatory Networks. Just focusing on the roles of micro RNAs (miRNAs), they have been found to be part of several intra-pathway regulatory motifs. However, inter-pathway regulatory mechanisms have been often neglected and require further investigation.Homo Sapiens pathways and concurs in creating a complex regulatory network involving several pathways connected by this specific motif. The role of this motif seems also confirmed by a deeper review of other research activities on selected representative pathways.In this paper we present the result of a systems biology study aimed at analyzing a high-level inter-pathway regulatory motif called Pathway Protection Loop, not previously described, in which miRNAs seem to play a crucial role in the successful behavior and activation of a pathway. Through the automatic analysis of a large set of public available databases, we found statistical evidence that this inter-pathway regulatory motif is very common in several classes of KEGG Although previous studies suggested transcriptional regulation mechanism at the pathway level such as the Pathway Protection Loop, a high-level analysis like the one proposed in this paper is still missing. The understanding of higher-level regulatory motifs could, as instance, lead to new approaches in the identification of therapeutic targets because it could unveil new and “indirect” paths to activate or silence a target pathway. However, a lot of work still needs to be done to better uncover this high-level inter-pathway regulation including enlarging the analysis to other small non-coding RNA molecules. Systems biology is increasingly highlighting that a discrete biological function can only rarely be attributed to a single molecule. Instead, most biological characteristics arise from complex interactions among the cell’s numerous constituents, such as proteins, DNA, RNA and small molecules -3. UnderThe fast development of technologies to collect high-throughput biological data allows us to determine how different molecules interact with each other, leading to a proliferation of biological networks . Several public and commercial network repositories including the WikiPathway database ,5, the ISeveral results have been achieved in the past few years from the research of recurrent motifs in complex Gene Regulation Networks -18, highFigure When a PPL is present, its action is carried out in the following steps:1. When the pathway is activated, one or more pathway genes co-express one or more intragenic miRNAs.2. The intragenic miRNAs expressed by the pathway target one or more transcription factors of some of the PAGs. We call these transcription factors Pathway Antagonist Transcription Factors (PATFs). In some situations, the pathway intragenic miRNA may also target the pathway itself, but this mechanism belongs to well-studied intra-pathway regulations that are not the focus of this work.3. The down-regulation of the PATFs has a repressive effect on the expression of the corresponding PAGs.4. Down-regulated PAGs have lower ability to express the Antagonist miRNAs. It is worth here to remember that miRNAs have a post-transcriptional regulation role. Intragenic miRNAs that directly target the PAGs would not actually prevent the production of the related Antagonist miRNAs since miRNAs are expressed during transcription. The only way PPLs can form is therefore by mediating the PAGs down-regulation through their corresponding PATFs .5. The reduced presence of Antagonist miRNAs contributes to the successful expression of the pathway genes, thus closing the protection loop.An interesting characteristic of PPL is its hierarchical structure: a very small number of intragenic miRNAs  is able to “defend” the expression of either a large number or even the most important pathway genes.This paper proposes an extensive systems biology study to analyze the existence and the characteristics of this new motif on a large set of public available networks. Results will show statistical evidence that this inter-network regulatory motif is very common in several classes of considered networks, and it can be used to identify an intricate set of links among networks thus building a high-level pathway to pathway interaction network. Finally, to further support the proposed research activity, literature mining allowed us to find clues of possible dysregulated PPLs in several papers targeting the study of tumors ,26.Homo Sapiens species available in the KEGG database [To assess the presence of PPLs we analyzed a set of networks for the database .Homo Sapiens species and represents one of the most curated and reliable source of pathway information. KEGG is unique for its focus and coverage of yeast, mouse, and human metabolic and signaling pathways [The KEGG database contains a set of 203 networks related to the pathways . All 203The final set of considered KEGG pathways is reported in the two files Additional file size) and the standard deviation (σsize) of the size of all networks in the KEGG dataset and by sampling a Normal distribution N to obtain random network sizes comparable with the real ones. Genes composing each network have been then randomly selected from the Sanger Genecode database release 9 (Sanger) [All pathways have been analyzed to search for the presence of PPLs resorting to a bioinformatics pipeline featuring the aggregation of information from several public on-line biological databases . To statistically analyze the existence of PPLs in the selected pathways we compared the obtained results with the ones gathered analyzing a population of randomly generated pathways. We generated a random population of 100 randomized networks. The size of each random network has been selected by first computing the mean (μ(Sanger) .We analyzed data obtained from our analysis using the R language and its environment for statistical computing . The fulWe first investigated if there is significant statistical difference in the frequency pathways manifest a PPL among the three considered groups of pathways . Table In order to find relationships among groups of pathways, frequencies reported in Table χ2 = 38.8678, d.f. 2, p = 3.631× 10-09), thus confirming our hypothesis that PPLs manifest with different frequencies based on the considered groups. To better understand where differences among groups lie, post-hoc analysis has been performed. We performed a chi-squared test considering all possible pairs of groups . Analyzing the obtained results reported in Table χ2 = 36.7867, p = 3.953649 × 10-09). This insight is in accordance with the study done in [χ2 = 11.9768, p = 1.077333 × 10-03) confirming our hypothesis that the establishment of this motif is not due to chance. Moreover, the frequency of pathways with PPLs is higher in the random group compared to the metabolic group . Again, this is non surprising at all. As already stated, metabolic pathways are in large part formed by metabolites unable to express the intragenic miRNAs required to create a PPL. Differently, random pathways include nodes which are randomly sampled from the full set of genes available in the Sanger Genecode database and have a higher probability to include genes expressing miRNAs potentially able to establish a PPL.Pearson’s Chi-squared test among the three groups points out that there is significant statistical dependence between rows and columns of the contingency matrix reported in Table -16). Eventually, the Kruskal-Wallis test, a non-parametric analysis of variance [To further analyze the characteristics of the proposed motif, we also investigated if we can observe statistical difference in the number of PPLs per pathway among the different groups . Again to better understand the differences among the different groups we performed post-hoc analysis running a set of Mann–Whitney U tests among pairs of different groups applying Holms p-value adjustment resorting to the R pairwise.wilcox.test procedure. Results of this analysis, reported in Table -09) and random (p = 0.0028) pathways. The same way, metabolic pathways manifest a lower number of loops compared to random pathways (p = 0.0028) further confirming the previous analysis on frequency of networks manifesting PPLs.Kruskal-Wallis rank sum test underlines that there is statistical difference in the number of loops among the three groups of pathways  thus reducing the number of predicted targets. Aggregated data for this mirSVR threshold are available in the Additional file 2 = 36.3039, d.f. 2, p = 1.308 × 10-08), thus confirming that even reducing the set of miRNA targets to the ones with higher score we still observe that PPLs manifest with different frequencies based on the considered groups. Table -08), and this difference is confirmed also in Table Pearson’s Chi-squared test on the contingency matrix reported in Table This outcome is particularly interesting since it highlights that the identified PPLs mainly involve high-score miRNA gene predictions, thus adding reliability to our findings.http://www.testgroup.polito.it/index.php/component/k2/item/184-ppl-list we built the network reported in Figure After performing statistical analysis on the presence of PPLs in this section we analyze how PPLs actually constitute a miRNA based inter-network regulation mechanism that can be used to build a complex network of networks that involves several pathways. Starting from the full list of identified PPLs that is available at Each node of the network represents a KEGG pathway. Two types of nodes are available: (1) hexagonal nodes represent pathways in which PPLs have been detected, (2) rhomboidal nodes are pathways in which no PPLs have been detected but containing at least one PAG. A directed weighted edge connects two pathways if a PPL generated from the first pathway targets a PAG contained in the second pathway. The weight of the edges represents the number of PPLs connecting the two pathways. Furthermore, each node is labeled with an additional parameter reporting the number of PAGs of the pathway that have not been detected in any of the KEGG pathways.The network reported in Figure By analyzing the nodes generating PPLs using the Cytoscape network analyzer plugin, it is also possible to highlight that on average, each pathway generating PPLs is connected to 25.111 pathways thus confirming the complexity of the identified motif, which involves the cooperation of several pathways.This enforces the idea that miRNAs cover different, and often even conflicting, roles in gene regulatory networks. From this perspective, we can identify three important and complementary roles for miRNAs in gene regulation: the first is the well-known intra-pathway regulatory role targeting genes belonging to the pathway itself; the second is an inter-pathway down-regulatory effect, where miRNAs expressed by a pathway directly silence mRNAs from genes belonging to pathways that may be biochemically or functionally incompatible with the pathway that is being expressed; the third is an indirect up-regulatory function where miRNAs, thanks to the PPL motif, indirectly contribute to the pathway up-regulation by down-regulating the Transcription Factors of its PAGs.In the remaining of this paper two pathways manifesting the PPL motif will be analyzed in detail. The full list of pathways where PPLs have been identified has been provided as additional material to this submission in the form of dot graph files.http://www.genome.jp/kegg-bin/show_pathway?hsa04150).The simplicity of the PPL protection mechanism can be appreciated in Figure The mTOR signaling pathway has been identified as a hub. It integrates the output of several upstream pathways, including insulin, growth factors and amino acids , as wellInterestingly, the RICTOR gene appears as an actor in the PPL identified within the mTOR pathway. As shown in Figure The most common pathological rearrangements of MLL , t, t and t) may mislead the proper miR-1976 regulatory function because the MLL translocation may imply changes in its miRNAs binding sites. Popovic et al.  showed thttp://www.ebi.ac.uk/gxa/). The expression of both RSK and RICTOR appears compatible with the identified PPL: (1) RSK  shows an upper regulation in ALL (p = 0.002), and it may confirm the attempt of the pathway to protect its correct behavior maximizing the production of protective miR-1976 by over-expressing its host gene; (2) RICTOR  appears globally down (p = 9.07e - 4), accordingly with the observed miR-196b up-regulation.To further validate these observations, we analyzed the expected level of expression of interactors in ALL disease retrieved from Gene Expression Atlas . Relaxing this threshold may identify additional PPLs with weaker target affinity. In this case, we identified three additional low-score miR-1976 targets in the mTOR pathway, which are PAG Transcription Factors involved in PPLs. However, their role and possible involvement in the disease dynamics are still under investigation.http://www.genome.jp/kegg-bin/show_pathway?hsa04612) reported in Figure Another very interesting example of pathway including PPLs can be found in the antigen processing and presentation (APP) KEGG pathway (#hsa04612-APP is composed of two inner pathways responsible for synthesis of major histocompatibility complexes I and II (MHCI and MHCII), which are responsible for cell destruction (when MHCI expression is low) and specific immunization (MCHII).The NFY complex is well known for peptide presentation in antigen presenting cells  and for miR-30e, located in the intronic region of NFY-C and co-transcribed with its host gene, has been highlighted as responsible for maintaining differentiated cell phenotypes. For instance, the knock out of miR-30 miRNA family induces epithelial-mesenchymal transition of pancreatic islet cells . MoreovemiR-30e directly targets the STAT1 transcription factor that belongs to the signal transducers and activators of transcription family. STAT1 is involved in up-regulation of genes (interferon stimulated genes) in response to different interferon based stimulation. In particular, after IFN-γ stimulation, STAT1 forms homodimers or heterodimers with STAT3 for binding with GAS (interferon-gamma activated sequence) promoter elements and their further regulation . This feSTAT1 promotes two PAGs: RUNX1 and UGT8. RUNX1, also known as AML1 or CBFA2, is a transcription factor that regulates the fate of hematopoietic stem cell populations and is generally regulated by 2 enhancers, which are tissue specific and drive the binding of lymphoid or erythroid regulatory proteins . RUNX1 tUGT8 encodes for an enzyme involved in glycosphingolipids synthesis, in particular galactosylceramides , which are involved in a variety of cellular processes including differentiation, cell-cell interaction, and transmembrane signaling ,59. It iFurthermore, previous studies highlighted that an induced dose-dependent inhibition of GalCer expression on the cell surface, after treatment with recombinant gamma-interferon (rIFN-γ), caused reduced viral (HIV- 1) infection by decreasing GalCer synthesis and expression . This mamiR-802, co-expressed with RUNX, targets multiple pathway genes: IFN-γ, NFY-C, CANX, and the HLA-DM complex. It is worth noticing that among its targets we find NFY-C, which is responsible for the PPLs initiation. CANX is a chaperone protein responsible for protein folding and quality control. It retains unfolded or mis-folded proteins in the endoplasmic reticulum, in order to have only well assembled proteins in the cytoplasm. CANX also controls the folding of the MHC class I alpha chain. This central role in the MHCI synthesis makes it a possible critical target in PPL dysregulation. The HLA-DM protein, another chaperone, finally, is targeted by both miR-802 and miR-577. HLA-DM regulates the peptides that bind to MHCII, and controls/presents the antigen in antigen presenting cells. It plays a central role in the MHCII complex stability by favoring more stable peptide-MHC complexes. Dysregulation of HLA-DM is associated with negative prognosis in breast cancer, since patients with tumors that co-express HLA-DR, Ii and HLA-DM have improved recurrence-free survival as compared with patients with tumors that express HLA-DR and Ii in the absence of HLA-DM , and, acThe discovery of the Pathway Protection Loops is suggesting a level of transcriptional regulation at the pathway level not fully investigated before. Studies conducted on specific miRNAs such as the one published by Barik  confirm The understanding of this and other higher-level regulatory motifs could, for example, lead to new approaches in the identification of therapeutic targets because it could unveil new and “indirect” paths to activate or silence a target pathway.A lot of work still needs to be done to better uncover this high-level inter-pathway regulation. miRNA are not the only small RNAs that are involved in regulatory mechanisms. For example, ceRNA have been recently identified as miRNA down-regulators . UnfortuTo study the characteristics and properties of PPLs, we designed a software pipeline that, combining pathway data available via PathwayAPI  with thehttp://www.testgroup.polito.it/index.php/bio-menu-tools/item/185-pathway-rotection-loops-finder) has been implemented as a collection of PHP classes, given the need of interfacing our software with several web based sources of information. All collected data have been saved into a unified relational database used for mining information about PPLs.The full software pipeline that is available at  ,2. the miRBase database release 18 (miRBase) 3. the microRNA.org database (microRNA.org) ,72,4. the TargetMine database ,5. the Transcription Factor encyclopedia (TFe) , and6. the Sanger Genecode database release 9 (Sanger) .In several cases, these databases use different convention for identifying specific entities . Whenever possible, information from the cited databases have been dumped into a local unified relational database and all entries have been then preprocessed to unify the different identifiers. Working with a local dump of the information also allowed us to speed-up the information retrieval process which requires a massive access to these information sources.To identify miRNAs co-expressed with the pathway genes we restricted our search to the set of intragenic miRNA. Intragenic miRNA represent around 50% of the mammalian miRNAs -79. MostWe assume that intragenic miRNA are in general co-expressed with their related host-genes as supported by previous studies ,80-83. RIntragenic miRNAs are retrieved through the miRBase database. miRBase is a searchable database of published miRNA sequences and annotations. About 94.5% of the available mature miRNA sequences considered in this paper have experimental evidence, thus representing a reliable source of information. Each miRNA entry in miRBase is correlated with the related information on the genetic location that is exploited to identify the host genes.To identify intragenic miRNA of a given host gene we first search for the coordinate of the gene using the e-Utils. Once obtained the gene coordinates we search for all miRNAs with coordinates embedded in the ones of the gene resorting to miRBase.We searched for potential targets of each identified intragenic miRNA resorting to microRNA.org. microRNA.org searches for miRNA targets applying the miRanda algorithm . The miRTo work with reliable predictions and limit the amount of returned miRNA-gene interactions, during the analysis we restricted our search to the microRNA.org “Good mirSVR score, Conserved miRNA” and “Good mirSVR score, Non-conserved miRNA” with negative mirSVR score lower than -0.3/-0.6. Given the selected intragenic miRNA name, searching for the targets simply requires an SQL query into the microRNA.org database.Antagonist miRNAs are miRNAs that target one of the genes of the pathway and similarly to the Intragenic miRNA targets can be retrieved through microRNA.org. Given the NCBI GeneID we query the microRNA.org database to identify the set of miRNAs targeting the gene. Query to microRNA.org at this step follows the same filtering rules on the mirSVR score applied for the identification of the intragenic miRNA targets.The identification of an antagonist miRNA host gene follows an inverted flow compared to the one employed to identify the pathway intragenic miRNAs. For each antagonist miRNA we identify the related coordinates using miRBase, and, given the coordinates, we search into Sanger for a gene whose coordinates embrace the one of the considered miRNA. Genes identified at this step represent potential PAGs.As already mentioned in the introduction of this paper, miRNAs have a post-transcriptional regulation role . IntrageBoth TargetMine and TFe provide web services to access the related database. To speed up the analysis all information contained in these two repositories have been downloaded and merged into a single database table containing relations between TFs and related target genes.To download the information contained in TargetMine, we retrieved from Sanger the full list of NCBI GeneIDs considered in our analysis. For each geneID we then searched for TF targeting the selected gene through the REST service http://targetmine.nibio.go.jp:8080/targetmine/service/template/results?name=Gene_TFSource&constraint1=Gene&op1=LOOKUP&format=xml&&extra1=H.+sapiens&value1=⟨targetgeneid⟩. The resulting xml formatted information has then been processed and integrated in the database.http://www.cisreg.ca/cgi-bin/tfe/api.pl?code=all-tfids. For each TF in the list, the list of targets has been computed calling the REST service http://www.cisreg.ca/cgi-bin/tfe/api.pl?code=entrez-gene-id&tfid=⟨TFID⟩. The resulted information has ben finally added to the local database and joined with the ones provided by TargetMine.A similar approach has been applied to download the information provided by TFe. The list of all TFs available in TFe has been downloaded through the REST service With the availability of a local database, searching for TFs targeting a given host gene simply requires to query the related database tables.The authors declare that they have no competing financial interests or other conflicts of interest.Alfredo Benso and Alessandro Savino coordinated the overall study and participated in the result validation. Stefano Di Carlo mainly worked on the statistical validation and Gianfranco Politano carried out the PPL analysis on the two selected pathways. All authors contributed equally to this work. All authors read and approved the final manuscript.This work has been partially supported by Grant No. CUP B15G13000010006 awarded by the Regione Valle d’Aosta for the project: “Open Health Care Network Analysis” and by the Italian Ministry of Education, University & Research (MIUR) .File containing aggregated data about the number of loops identified in the considered pathways. Analysis is performed with mirSVR < -0.3 threshold on the miRNA target identification.Click here for fileFile containing aggregated data about the number of loops identified in the considered pathways. Analysis is performed with mirSVR < -0.6 threshold on the miRNA target identification.Click here for fileCytoscape session file containing the network of interaction among pathways connected by PPLs. It requires Cytoscape v.2.8 to be displayed.Click here for file


Coronary heart disease (CHD) is a common medical problem in general practice. Due to its chronic character, shared care of the patient between general practitioner (GP) and cardiologist (C) is required. In order to improve the cooperation between both medical specialists for patients with CHD, a local treatment pathway was developed. The objective of this study was first to evaluate GPs’ opinions regarding the pathway and its practical implications, and secondly to suggest a theoretical framework of the findings by feeding the identified key factors influencing the pathway implementation into a multi-dimensional model.The evaluation of the pathway was conducted in a qualitative design on a sample of 12 pathway developers (8 GPs and 4 cardiologists) and 4 pathway users (GPs). Face-to face interviews, which were aligned with previously conducted studies of the department and assumptions of the theory of planned behaviour (TPB), were performed following a semi-structured interview guideline. These were audio-taped, transcribed verbatim, coded, and analyzed according to the standards of qualitative content analysis.We identified 10 frequently mentioned key factors having an impact on the implementation success of the CHD treatment pathway. We thereby differentiated between pathway related , behaviour related , interaction related , and system related factors . The overall evaluation of the CHD pathway was positive, but did not automatically lead to a change of clinical behaviour as some GPs felt to have already acted as the pathway recommends.By providing an account of our experience creating and implementing an intersectoral care pathway for CHD, this study contributes to our knowledge of factors that may influence physicians’ decisions regarding the use of a local treatment pathway. An improved adaptation of the pathway in daily practice might be best achieved by a combined implementation strategy addressing internal and external factors. A simple, direct adaptation regards the design of the pathway material , or the embedding of the pathway in another programme, like a Disease Management Programme (DMP). In addition to these practical implications, we propose a theoretical framework to understand the key factors’ influence on the pathway implementation, with the identified factors along the microlevel (pathway related factors), the mesolevel (interaction related factors), and system- related factors along the macrolevel. Despite the declining mortality of patients in recent years with coronary heart disease (CHD) in Western countries ,2, CHD rIn this context, an important role is attached to the shared care of patients with CHD by the general practitioner (GP) and the cardiologist. Better coordination and communication between GP and medical specialist promises optimized medical treatment along with increased cost-effectiveness in primary care -9. ImproTo date, most experience with treatment pathways is gained in countries with a Beveridge type of health care system, characterized by a strong governmental influence. In countries like Germany, where a Bismarck type of health care system is established , the impWithin this context of developing and evaluating a local CHD pathway we pursued two objectives in this study. On the one hand we aimed to evaluate GPs’ opinion regarding the pathway and give practical implications for clinical practice that derived from this evaluation. On the other hand we strived to abstract our findings by putting the identified key factors influencing the pathway implementation into a multi-dimensional model.In 2008, GPs and cardiologists from the Marburg region, Germany, were invited by the Department of General Practice at the University of Marburg to jointly develop a local treatment pathway for patients with CHD. By involving end-users in the pathway development and thus considering their experience and expertise of daily practice, we realized a bottom-up approach . It was The development of the pathway was based on current regional  and natiOur qualitative study was part of a larger feasibility study  with 18 In brief, the larger project aimed to improve the shared care of patients with CHD by GPs and cardiologists. The quantitative study investigated physicians’ adherence to pathway recommendations regarding the prescription of drugs and the referrals to the cardiologist, and its impact on patient variables such as satisfaction with the treatment and health-related quality of life. The analysis of the quantitative results and its relation to the qualitative data (triangulation) is in progress and will be reported soon. To gain further insight into GPs’ opinion regarding the pathway and the factors influencing GPs’ decision to (not) implement the CHD pathway, we conducted this qualitative study in the middle of the feasibility study so that physicians’ experience with the pathway could be ascertained. Face-to-face interviews were undertaken during February and March 2010 in the physicians’ practices by one of the authors (LK) who was not involved in the development of the pathway.In our study we aimed not only to report the results of the interviews and their practical implications, but to identify the latent pattern of the data. According to Sandelowski and Barroso, this more explanatory than exploratory interpretation of the qualitative data can be categorized as thematic/conceptual as we provide a theoretical framework of our findings . The resGPs and patients were informed in detail about the study and all gave their written consent to the study participation. Ethical approval for the study was obtained from the Ethics Committee of the Faculty of Medicine at the Phillips University of Marburg, Germany.Within the context of the feasibility study, we asked all 12 pathway developers (8 GPs and 4 cardiologists) and the 6 pathway users, familiar with the pathway recommendations, to participate in our qualitative evaluation study. Thus, by selecting respondents that were most likely to yield useful information about the key factors influencing the pathway implementation, we used a purposeful sampling strategy  and consThe interviews were based on a semi-structured interview guideline covering the attitude towards the developed pathway and medical innovations such as treatment pathways and guidelines in general, key factors that influenced the implementation of the CHD pathway in a negative or positive way, and suggestions of improvement and behaviour changes in consequence of the pathway. The interview guideline was developed jointly by two of the authors  and was aligned with previously conducted studies from our department on changes of professional behaviour ,35 and tInterviews were audio-taped and transcribed verbatim. The accuracy of transcripts was checked prior to being transferred to the computer software program Maxqda 2007 , which aTwelve pathway developers  and four pathway users consented to conducting face-to-face interviews. The reason for non-participation of 2 GPs in the user group was a lack of time. The majority of the interviewees were male (75 %), between 41 and 50 years of age (56 %), and employed in full-time practice (94 %). Most of the interviewed GPs and cardiologists (75 %) strongly agreed with the pathway. Further characteristics of the participants are summarized in Table As shown in Table As this study was undertaken to gain additional insight into the relevant key determinants for implementation, we focused a cross-case analysis on the identification of general factors and were less interested in interindividual differences.The first major theme in this category dealt with the pathway content. Many of the participating GPs and cardiologists (C) considered the CHD pathway as a useful and high quality treatment aid.“The treatment pathway is good to create a standard framework of how to interact with certain patients, so that it is clear for everybody. I think it’s a reasonable thing.” [C1]“I think it’s actually useful to have such an aid. It improves your awareness for things you should keep an eye on. To my delight, the number of beta blockers was reduced and more certainty was provided regarding the sequence of cardiological consultations.” [GP2]The effort created by the implementation of the pathway in daily practice was perceived as controversial by the interviewees. While the case record form (CRF) documentation for the evaluative study was assessed as time consuming, the application of the pathway in practice was rated as time saving, or no difference was recognized.“It was time consuming to somehow record [the patients’ medication history]. The second time around it was no problem because in most cases nothing had changed. You could copy [the information] or just refer to the first record. Integrating [the information] is not a problem now, unlike the first time.” [GP6]“I would say that [the pathway] certainly saves time, [especially] for colleagues who haven’t followed [the pathway’s recommendations] or have yet to really put the drug therapy into practice.” [GP1]A relevant issue for the participating physicians was the balance between ‘cookbook medicine’ by a high adherence to the pathway’s recommendations and an individual treatment of the patient. On the one hand, participants appreciated the standardised treatment guideline, on the other hand, they emphasized the consideration of intuition and experience in the consultation.“It is indeed like cookbook medicine, but on the other hand, it makes one feel safer. And I think that every one of us has a certain plan of how to proceed for every clinical situation. And those [plans] could be standardized. Then you can say ‘Ok, we consistently proceed according to this model, to this scheme.’ I think this is good.” [GP7]“As a doctor, I want to maintain individual treatment. I am the one who decides based on my twenty years of experience in cardiology. I do not want the pathway to tell me what I have to do. Guidelines are only guide rails within which you can act.” [C1]The participation in the development of the pathway was an important factor for the physician’s evaluation of the CHD treatment tool. Overall, the participation resulted in a positive evaluation, even though concessions had to be made.“Our behaviour informed the pathway, like always. I support [the pathway]; we worked on [its development] for a long time. It was extensively discussed before everybody could agree on its final version. So I perceived [the pathway development] as positive.” [C2]“You are not bound to a guideline that was imposed from above, but in whose development we took part, and which we could influence. This is what makes it good. If you contribute to such a thing, then it is something you support. My ideas are included and the way I think you should act. And, therefore, I am totally in support of it.” [GP1]“We could not enforce [the frequency of cholesterol measurement]. When you make decisions as a group you have to swallow some bitter pills.” [C3]The appraisal of their own behaviour in the past was an important factor for physicians’ willingness to implement the pathway. Many interviewees felt that they had treated their patients with CHD according to the pathway recommendations during previous visits.“[Our behaviour] has not changed a lot. We have not invented something new, but have, in principle, implemented the guidelines.” [GP5]“Actually, I acted like I always did: the patients came in regular intervals, we talked about the disease, we talked about incidents, we talked about the cardiological report, and we checked the laboratory parameters and drugs.” [GP6]The participating GPs expressed a desire to integrate the pathway recommendations into practice software. A PC integration was perceived as facilitating the embedding of the pathway in daily practice, e.g., the schedule for monitoring visits. Concerning the need for regular refresher meetings, the physicians had heterogeneous opinions: On the one hand the knowledge update was appreciated; on the other hand regular meetings were seen as too time-consuming or simply unnecessary.“Of course we would keep recommendations in mind with regular training sessions, but we already have so many continuing medical education (CME) events. So it is not necessary.” [GP3]“I think it’s reasonable to have regular trainings so that it becomes second nature [to us doctors]. I think this is very important.” [GP1]Patients’ acceptance of and compliance with the pathway recommendations were rated high by the interviewees if the pathway recommendations were thoroughly explained. Furthermore, the participating physicians reported that knowledge of the patient’s background, e.g., medical history and psychosocial conditions, may influence the application of the pathway. This can be reflected in either patient related assumptions or routine actions in the consultation.“And for the patient it [the pathway] is actually better. He stays more compliant because he knows it is not back and forth but a routine program that he gets every time. This makes the patients feel safer.” [GP8]“Of course we act according to the disorders and the risk profile, but we know the patients from their domestic conditions and whether they comply or not. And in these cases I would vary [the treatment].” [GP9]“Being aware gets lost sometimes, especially when you have had patients for years or take over a practice and there are listed diagnoses and drugs, so you do not always question everything, you just overlook things.” [GP2]The cooperation between GPs and cardiologists was appreciated by both sides. Apart from smaller disagreements concerning coordination responsibility for the CHD patients, or adherence to the pathway advice, the collaboration was described as positive.“We actually understood it as the treatment pathway helps us to do our job, and that one part of that - what we have done before - is connected to the primary care treatment. If we collaborate with competent GPs, we already tend to tell our patients that they have a competent GP to whom they should primarily turn to, e.g., when deterioration happens.” [C3]“No problem now, [the cooperation] has improved considerably. The cardiologists do not make it their responsibility to schedule monitoring visits, instead leaving it more to the GP`s discretion. [Cardiologists] complained that many [patients] come to unnecessary monitoring visits, but now I think it is working better.” [GP10]By integrating the pathway into a larger geographic, such as nationwide or organizational contexts, e.g., a medical association, the interviewed physicians expected an increased implementation success of the tool. The overlap between the pathway and the German CHD Disease Management Program (DMP) as systematic treatment program for chronically ill people ,39 was s“But I think that one can and should implement [the pathway] at the health care system level. One could, for example do [the pathway implementation] with our medical association and raise it to another level.” [C2]“Because we are bound by contract to the DMP, we are obligated to act correspondingly. Even if we found the treatment pathway more reasonable, we are forced to implement the DMP. Sanctions are partially imposed if you do not participate in the DMP.” [GP7]Participants perceived the actual demands of the German health care system as influencing the implementation of the CHD pathway. Due to competing tasks in daily practice, the pathway would be at risk of being forgotten. The GPs named their small gate-keeping influence in the German health care system as influencing factors for appointments with medical specialists as patients have free access to specialised care.“We have so many fires burning at the moment, so many contracts, special regulations and treatment plans, DMP situations and such, that we partly lose the big picture. That makes it difficult for us.” [GP7]“Thus, there is so much to work on at the moment. I think that is the main reason for not optimally meeting the requirements of every patient.” [GP9]“I actually see another essential problem in, such is it here, we have free choice of care and many people go to secondary care on their own decision.” [GP7]Our study complements the understanding of potential facilitators and barriers to be considered in planning the implementation of innovative tools like the CHD pathway and is in line with the findings of other international studies ,16,17,40Concerning the treatment pathway itself, we first assessed the pathway content, the participation in the pathway development/ownership, the individual flexibility, and the related effort as relevant aspects for the implementation. The overall evaluation of the pathway recommendations was positive and required no additional effort by some of the GPs. Referring to the pathway development, which was appreciated by the physicians, a study by Grimshaw and colleagues showed that the participation in developing a guideline could enhance the compliance in physicians by about 32 %, whereas it was 22 % for non-developers . AdditioIn addition to the aspects related to the pathway, we identified further relevant factors. On a behaviour-related level we identified previous behaviour and support as important factors for pathway implementation. Located on an interactional level and consistent to other research, the patients’ acceptance of the pathway and the physicians’ cooperation with their colleagues were assessed as important components for the implementation of the pathway . PatientFinally, on a system level, an effective use of the CHD pathway depends on both the health care system and the context in which the medical implementation occurs.Considering the impact of contextual factors for the successful implementation of medical innovations is an aspect also mentioned by other authors ,46. We aEven though the overall evaluation of the pathway recommendations was positive, this appreciation did not automatically lead to a behaviour change as some physicians reported to have already been treating their patients as the pathway recommends. According to the theory of planned behaviour (TPB) , attitudAs mentioned by the physicians, the implementation of the pathway would be eased by its integration into PC software and thus simplify and standardize recall possibilities. Another factor influencing the pathway implementation are competing recommendations of the pathway and the German DMP, as the DMP includes mostly similar, but sometimes aberrant recommendations and different timetables. A possible practical implication would be to embed the pathway recommendations directly in a DMP, or taking the DMP recommendations into account during the process of pathway development.In addition to reporting our findings and giving some practical implications, we aimed to abstract our results and give a theoretical framework (see Figure We thereby considered two dimensions: temporal sequence and content. We assumed that the key factors influencing the implementation of the CHD treatment pathway can be classified according to their impact in the pre-intervention, e.g., previous behaviour, the present-intervention, e.g., pathway material, or the post-intervention period, e.g., support. Referring to the Ecological System Theory by Bronfenbrenner  and baseWe do not claim completeness of the aforementioned factors or categories, nor do we provide an algorithm of how the presence or absence of the reported key factors attributes to the relative implementation success of the pathway. Our interest was to propose a theoretical framework that might serve as background for further implementation research.As a result of the qualitative study design and the small study sample, the representativeness of the data may be limited. In addition, the selection bias caused by the fact that only motivated and interested physicians participated in the study may influence the representativeness of the results. A possible bias by social desirability in the face-to-face interviews cannot be excluded. Nevertheless, due to the meaningful results and conclusions gained in a Bismarck type of health care system, we presume the chosen method to be appropriate for our research question -55, whicAnother limitation concerns the proposed multi-dimensional model. The distinction between the time- and level-related categories should not be seen as static. The model is a draft with flexible confines between the singular categories and has to be proven in further research. For example, due to its actual impact, we related the pathway development to a present point in time; putting this factor in the past would have been another possibility. We did not investigate possible differences in the response patterns of pathway developers and pure users, or GPs and cardiologists, while the research question focused on the identification of general factors influencing the pathway implementation rather than on interindividual differences.As our research dealt primarily with external implementation factors and final outcomes, the role of underlying psychological and interactional processes will need to be investigated in future studies. In addition to cognitive or affective processes like self-efficacy expectancy , generalOur study demonstrated that the implementation of medical innovations like the reported CHD treatment pathway is influenced by multiple components. Besides reporting the factors influencing the pathway implementation and giving practical implications how to improve pathway implementation, we proposed a theoretical framework by arranging the findings in a multidimensional model and thereby add innovative aspects to the already existing implementation research. Fields scarcely investigated collectively in Bismarck type of health care systems, like bottom-up developed local pathways and the integration of primary and secondary care, are considered.The authors declare that they have no competing interests.All authors contributed to study design. LK and NDB developed the interview guideline. LK conducted the interviews. Data coding and analysis were coordinated and performed by LK and KS. LK drafted the manuscript. KS, ST and NDB provided critical review on all parts of the manuscript. All authors read and approved the final version of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2296/13/36/prepubPocket version of the CHD treatment pathway.Click here for file


Summary: Most current stable isotope-based methodologies are targeted and focus only on the well-described aspects of metabolic networks. Here, we present NTFD (non-targeted tracer fate detection), a software for the non-targeted analysis of all detectable compounds derived from a stable isotope-labeled tracer present in a GC/MS dataset. In contrast to traditional metabolic flux analysis approaches, NTFD does not depend on any a priori knowledge or library information. To obtain dynamic information on metabolic pathway activity, NTFD determines mass isotopomer distributions for all detected and labeled compounds. These data provide information on relative fluxes in a metabolic network. The graphical user interface allows users to import GC/MS data in netCDF format and export all information into a tab-separated format.Availability: NTFD is C++- and Qt4-based, and it is freely available under an open-source license. Pre-compiled packages for the installation on Debian- and Redhat-based Linux distributions, as well as Windows operating systems, along with example data, are provided for download at http://ntfd.mit.edu/.Contact:gregstep@mit.edu Indeed, such methodologies are often incapable of discovering new phenomena that have yet to be described in detail. Furthermore, quantification of metabolites can be of limited use when characterizing dynamic processes like metabolism. Metabolic fluxes provide the ultimate readout of in vivo enzyme activity . Thus, NTFD provides a straightforward framework for combining the systems-level capabilities of traditional metabolomics approaches with the kinetic information that can only be ascertained through the use of isotopic tracers.Here, we present a software, non-targeted tracer fate detection (NTFD), which detects all observable metabolites labeled by a stable isotope tracer within a GC/MS dataset , which can be further processed with spreadsheet or statistics applications.Funding: Supported by the Fonds National de la Recherche (FNR), Luxembourg (ATTRACT A10/03 & AFR 1328318) and the German Research Foundation (DFG)HI1400/1-1.Conflict of Interest: none declared.


Follicle stimulating hormone (FSH) is an important hormone responsible for growth, maturation and function of the human reproductive system. FSH regulates the synthesis of steroid hormones such as estrogen and progesterone, proliferation and maturation of follicles in the ovary and spermatogenesis in the testes. FSH is a glycoprotein heterodimer that binds and acts through the FSH receptor, a G-protein coupled receptor. Although online pathway repositories provide information about G-protein coupled receptor mediated signal transduction, the signaling events initiated specifically by FSH are not cataloged in any public database in a detailed fashion.We performed comprehensive curation of the published literature to identify the components of FSH signaling pathway and the molecular interactions that occur upon FSH receptor activation. Our effort yielded 64 reactions comprising 35 enzyme-substrate reactions, 11 molecular association events, 11 activation events and 7 protein translocation events that occur in response to FSH receptor activation. We also cataloged 265 genes, which were differentially expressed upon FSH stimulation in normal human reproductive tissues.http://www.netpath.org), a pathway resource developed previously by our group.We anticipate that the information provided in this resource will provide better insights into the physiological role of FSH in reproductive biology, its signaling mediators and aid in further research in this area. The curated FSH pathway data is freely available through NetPath ( Follicle stimulating hormone (FSH) is a glycoprotein hormone secreted by cells called gonadotrophs in the anterior pituitary gland. The major function of FSH is to promote and sustain the ovarian follicular growth in female  and sperFSH is a member of glycoprotein hormone family, which also includes LH, human chorionic gonadotropin (hCG) and the thyroid stimulating hormone (TSH). These hormones are heterodimeric proteins comprised of a common α subunit and a unique β subunit that confers the biological specificity to each hormone. The receptors of these hormones belong to the G-protein coupled receptor (GPCR) superfamily, members of which contain seven transmembrane alpha helical domains .Binding of FSH to its cognate receptor, FSH receptor (FSHR), triggers rapid activation of multiple signaling cascades. FSH action is primarily mediated via activation of the receptor associated heterodimeric G-proteins, which results in stimulation of adenylate cyclase activity and production of cyclic AMP (cAMP) . Consequhttp://stke.sciencemag.org/cm/), KEGG Pathway Database (http://www.genome.jp/kegg/pathway.html), Pathway Interaction Database of the National Cancer Institute (http://pid.nci.nih.gov/) and INOH Pathway Database (http://www.inoh.org/) contain generic information about GPCR signaling. None of these resources provide information specific to FSH-induced signaling. Because of the importance of this pathway in human physiology and disease, we undertook a systematic effort to gather and curate the relevant information available in published literature to create a public resource of the signaling events triggered by FSH.Although FSH signaling has been relatively well studied, there is a dearth of resources, which provide comprehensive information about the molecules involved in the FSH signaling pathway, the transcription factors activated by FSH and the genes whose expression is eventually regulated. Several online pathway databases including The Database of Cell Signaling  proteins involved in reactions should be from human system, however other mammalian proteins were annotated if they were not reported from human system; and, (iii) only experiments carried out in cell lines of mammalian origin were considered.FSH signaling pathway reactions have been curated in three categories-molecular associations, enzyme-substrate reactions and protein transport reactions. The curation of FSH pathway reactions were based on the following criteria: (i) reactions should be stimulated by FSH/FSHR system The molecular association reactions include both direct (binary association) and complex (multimeric association) protein-protein interactions stimulated by activated FSHR. The enzyme-substrate reactions induced by FSH were curated as direct (where the immediate upstream enzyme responsible is known) or induced (where the immediate upstream enzyme is not known) reactions. The protein sub-cellular translocation events induced by FSH were curated as transport reactions. In many studies, the authors investigate only the activation status of molecules under stimulation with the ligand. These reactions which cannot be categorized as either molecular associations or enzyme-substrate reactions were documented under a separate category called activation/inhibition reactions. Reactions in the FSH pathway were curated from literature dealing with studies carried out in mammalian host systems with the reactions reported in humans preferred over mouse, rat, bovine or other mammals.http://www.netpath.org/pathways?path_id=NetPath_25) and will be periodically updated. Figure The articles relevant to FSH induced signal transduction events were retrieved from PubMed. From these research articles, we documented 11 molecular association events, 35 enzyme-substrate reactions, 11 activation events and 7 protein translocation events that occur upon FSH stimulation. The site of post-translational modifications were also documented and mapped to RefSeq whenever this information was available. FSH pathway reactions are available in NetPath at (http://www.celldesigner.org/) to generate the pathway map in the SBGN format . A detailed description of the NetSlim criteria is available at the same portal.There have been efforts by other groups to catalog and represent reactions of signal transduction pathways -23. TheyApart from the molecular association, catalysis and translocation events, we included information regarding the genes that are differentially regulated by activated FSHR in normal human reproductive tissues. We documented 265 such genes whose expression levels changed in response to FSH administration. Out of these, 166 genes were upregulated and 99 were downregulated following FSH induction of human ovarian granulosa cells. Five of the eight articles which were curated were high-throughput mRNA expression profiling studies. The differential expression of some genes was also validated using RT-PCR in two of these studies. We included data only from studies carried out in normal human cells/tissues. A minimum fold change value of 2 and a p-value < 0.05 were taken as arbitrary cutoffs for inclusion into the resource. Data was not annotated from studies carried out in any disease setting. We also excluded studies where other hormones such as LH or hCG were used along with FSH for granulosa cell stimulation as there was ambiguity about the genes regulated by individual hormones. We have also documented several instances where specific transcription factors induced by FSH were shown to control the expression of differentially regulated genes.http://www.biopax.org/) [http://sbml.org/), a computer-readable format for representing models of biological processes [http://www.netpath.org/pathways?path_id=NetPath_25. The SBML format of the map given in Figure The FSH pathway data is compatible with various standard data exchange formats including Proteomics Standards Initiative Molecular Interaction XML format (PSI MI) , a standax.org/) , a standrocesses . This alrocesses  or Chisirocesses . The dathttp://www.netpath.org/pathway_authority?path_id=NetPath_25). We hope to continue this model for curation of pathways in the future where we involve experts on individual pathways as Pathway Authorities and include them as co-authors on descriptions of the corresponding pathways.Although the information in this resource is curated from published literature in accordance with our curation strategy and had been subjected to various levels of internal review, we consider it necessary that an expert in this area is involved to ensure its accuracy and comprehensiveness. For this pathway, the data was reviewed by S.M, who is one of the co-authors, and is the designated Pathway Authority (Studies of molecular events that result from activation of various receptors by their ligands are key to understanding various biological processes. Availability of such information serves as an invaluable tool for analyzing various kinds of high-throughput data obtained from gene/protein arrays as well as proteomic experiments. These analyses may include overlaying the high-throughput data onto known pathway reactions as well as perturbation analyses. We here describe the generation of a pathway map of the signaling events that are mediated by activated FSHR, through a systematic and detailed curation of the relevant published literature. This is aimed at providing a global and comprehensive view of the intracellular signaling events as well as the biological processes regulated by FSH. This would also enable researchers to perform various pathway as well as systems biology type of analyses on high-throughput data.The authors declare that they have no competing interests.AP designed the study. DT collected and annotated information into the resource. AA, SMP, SBD, RR assisted with the annotation. JS handled the computational aspects of the study. SM scrutinized and reviewed the pathway information as the Pathway Authority. TSKP, JM, YLR, SSM and RR also helped in reviewing the pathway reactions. DT prepared the manuscript. SM, TSKP, SMP, RR and AP edited the manuscript. All authors read and approved the final manuscript.FSH pathway map. A high resolution image of FSH pathway map drawn using CellDesigner software.Click here for fileSBML file of FSH pathway. The SBML file generated by exporting the FSH pathway map that was drawn using CellDesigner software.Click here for file