CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e.  CTCF, CACNB2, and ARL5B, i.e. genes involved in chromatin modulation, calcium channel signaling and membrane transport, respectively, were highlighted as candidate genes for schizophrenia risk.Large-scale genetic studies of complex diseases such as schizophrenia have identified a variety of susceptibility loci. Since many of the respective variants have only a weak influence on disease risk, pathophysiological interpretation of the results is problematic. Investigation of the joint effects of multiple functionally related genes or pathways increases the power to detect disease related genes, and provides insights into the etiology of the disease in question. In the present study, an integrated hierarchical approach was applied to: (i) identify pathways associated with complex neuropsychiatric disease schizophrenia (ii) detect potentially affected genes in these pathways; and (iii) annotate the functional consequences of genetic markers in the affected genes or their regulatory regions. Two samples comprising >10,000 individuals of European ancestry as well as data from the Psychiatric Genomics Consortium schizophrenia study were examined. Pathways representing transcriptional regulation and gene expression, cell adhesion, apoptosis, and synapse organization showed significant association with schizophrenia. In particular,  Genome-wide association studies (GWAS) have identified common susceptibility variants for numerous disorders At the time of writing, several methods are in use for the pathway-based analysis of GWAS data Various methodological approaches to pathway association analysis are available. Maciejewski While selection of the pathway association method is an important consideration, the power of a given pathway association study is also dependent upon other factors. These include the biological information (i.e. from gene-set and pathway databases) that is integrated into the model, the use of independent replication datasets, and the different levels of interpretation, which extend from the pathway level to the level of SNPs.As a logical consequence, researchers are now modifying analytical frameworks in order to increase their power and potential impact. To achieve this, the present study has applied a hierarchical approach see Figure 1Table S1A). Of these, 14 pathways remained significant in the replication dataset. The replicated pathways are listed in Figure S1. Table S2 summarizes the redundancy estimates for pathways retrieved from the same source. A description and a visual depiction of pathways with similar SNP content in the BOMA-UTR dataset are provided in Text S1 (section “Pathway overlap”) and Figure S2, respectively. The overall gene and SNP overlap between all pairs of replicated pathways are provided in Table S3A and Table S3C, respectively. For the GAIN-MGS dataset, the gene and SNP overlap information is provided in Table S3B and Table S3D, respectively. The section “Subject vs SNP label permutations” in Text S1 and Figure S3 provides a detailed description of the results of the SNP-label permutation test coupled with the subject-sampling test.Application of the Global Test to the BOMA-UTR (MooDS SCZ consortium (BOMA)) dataset and independent data from a Dutch study (UTR), To visualize the integration of the Global Test application on a SNP-, a gene- and a pathway level, Circos plots were generated for the entire genome Figure 2FOXP2 (eight pathways); BCL11A (six pathways); PCDH7 and RPL36P13 (five pathways respectively); and CACNB2, CTCF, MECOM, and RIMS1 (four pathways respectively).A total of 100 genes fulfilled the criteria described in the Methods section “Gene-based analysis with Global Test and FORGE”, i.e. these genes map to SNPs with a component Global Test p-value of <0.001 in the BOMA-UTR dataset. Of these, the following eight genes were annotated to at least four (up to eight) of the 14 replicated pathways, thus indicating their potential importance in terms of SCZ risk: Table S4). The vast majority of the 18 genes reside on different chromosomes, while most of the remainder reside on different chromosome arms. It therefore seems reasonable to assume that they represent independent signals, which results in a p-value of 0.004 for an enrichment of SCZ-associated genes among the 100 top genes. Included in the list of 18 replicated genes are known SCZ susceptibility genes, such as NRXN1, GRM3, and MMP16. Two of the eight most frequent genes in the top 14 pathways were also among the nominally significant genes in the gene-based FORGE analysis, i.e. CACNB2 (p = 8.57×10−4) and CTCF (p = 0.015). Given the overlap  between the PGC sample  and the present discovery sample , we opted to analyze the PGC dataset without including our discovery dataset. These analyses generated results of the same order of magnitude for both genes . While CACNB2 showed a trend towards association in an independent dataset from Denmark (p = 0.0970), thus supporting the strong signal from the PGC data, CTCF was found to be strongly associated in the same independent Danish sample (p = 0.0075).Of the genes that were annotated to the 14 replicated pathways, the top 100 were then tested in the Psychiatric Genomewide Association Study Consortium (PGC) data. Of these, significant results were obtained for 18 genes . CTCF and its regulatory regions. These include SNPs genotyped in the present discovery study and SNPs identified as their proxies using SNAP. For the latter, only those that were annotated by RegulomeDB as being (1) likely to affect DNA binding of the protein and linked to expression of a gene target, or (2) likely to affect DNA binding, are listed. The complete functional annotation data for the SNPs of CTCF are provided in Table S5. All genotyped SNPs annotated to CTCF showed a significant  contribution to pathway associations. Of these, rs6499137 and rs7191281 were located at the 3′-UTR and the intron of CTCF, respectively. Given the 20 kb flanking region allowed for assigning the SNPs to a gene, the other two SNPs were considered to be shared with the neighboring gene RLTPR. Based on the functional annotation with the RegulomeDB database, the 3′-UTR SNP of CTCF (rs6499137) and its proxies were considered to be associated with the altered expression of the neighboring gene RLTPR (Table S5). One of the proxies (rs17686899) overlaps with a number of functional elements, such as open chromatin region, the binding sites for different transcription factors, and regions with certain histone modifications across many cell types. This suggests that the SNP was likely to affect the binding of a number of transcription factors to the genomic region of this gene. The respective expression quantitative trait loci (eQTL) information suggested that the SNP was likely to affect the expression of two genes, i.e. DUS2L and RLTPR. Among the CTCF-annotated SNPs, the intronic SNP of CTCF, rs7191281, was one of the top SNPs  contributing to the association of CTCF (and the association of the four replicated pathways containing CTCF). In addition, this SNP had the lowest p-value in the analyses of the PGC SCZ sample. While no information concerning functionality was available in the RegulomeDB database for this intronic SNP of CTCF, its proxy, rs13334205, was annotated with strong functional consequences. This proxy SNP was located in the regulatory region of CTCF and overlapped with the binding site of DNA-binding proteins, such as EBF1, TCF12, POLR2A, in an open chromatin region (Table S5).Polyphen-2 predicted that the coding SNPs of interest in CACNB2 are provided in Table S6. The positions of the majority of the genotyped and the proxy SNPs of CACNB2 overlapped a motif match to the FOX  and GATA  family motifs in open chromatin regions. Among the SNPs mapped to CACNB2, rs12257556 and its proxy rs4748474 were annotated with the strongest functional consequences. These intronic SNPs were eQTLs for ARL5B, and overlapped an open chromatin region. The proxy SNPs rs35803482 and rs7897710 both overlap with the binding sites of RAD21, SMC3, CTCF, and have a motif match for FOXP1. The intronic SNP rs2799573  lies in the binding region of a number of proteins, such as CDX2, CTCF, JUN, JUND, MEF2A, RAD21, and SMC3 (Table S6), as identified in the ENCODE ChIP-seq data across a diverse set of cell types.The complete functional annotation data for the SNPs of In the present study, a genome-wide pathway association analysis was performed by means of the Global Test. The analyses involved well-curated descriptions of 7,350 pathways, and were carried out on large-scale discovery and replication datasets. A gene-based analysis of genes with a high contribution to the significance of the top pathways was then performed using the SCZ GWAS results of the PGC. Finally, a functional SNP-based analysis of the top hit genomic regions was conducted. Through this hierarchical approach, we were able to replicate pathway findings from previous studies of SCZ and detect novel pathways and genomic regions with an association to SCZ in the investigated samples. In the discovery set, we detected evidence for a significant contribution of 27 pathways. Of these, 14 remained significant in the replication dataset. The 14 replicated pathways are involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis.Previous pathway analyses of SCZ GWAS data have identified associations with pathways that are mainly involved in processes critical to synaptic function, neurodevelopment, cell adhesion, the immune system, the estrogen biosynthetic process, and apoptosis Text S1 and Table S1C). The difference in results can be explained by the different assumptions these alternative pathway approaches rest on.However, the majority of pathways with significant association to SCZ in the present study are novel, and they are mainly involved in transcriptional regulation and gene expression. One reason for the failure of previous pathway-based studies of SCZ to generate similar findings may have been that they focused mainly on gene sets from the KEGG and BioCarta databases, whereas we accessed several pathway databases. These included the GO database, as well as special gene-set collections on chemical and genomic perturbations (dbCGP), and transcriptional regulation such as dbTFT and dbMIR. It should be noted that only few of our 14 replicated pathways achieved significance in the analysis of our discovery sample using GRASS As part of our hierarchical approach, we aimed to identify which genes in a particular pathway could be responsible for the association with SCZ risk. Integration of gene-based analysis facilitated both the prioritization of potential candidate genes and more precise formulation of hypotheses concerning the functional consequences of the potential pathway perturbations (i.e. at the gene- and SNP-level). In particular, we explored how variants that emerged as being of importance for our pathway- and gene-based signals might affect the function and regulation of other genes.CACNB2 and CTCF showed the strongest evidence for association with SCZ in both the present samples and in those of the PGC. The gene CACNB2 encodes an auxiliary voltage-dependent L-type calcium-channel subunit that is mainly expressed in heart and brain tissue CACNB2 was among four loci with genome-wide significance in a cross-disorder analysis of GWAS data for autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and SCZ CACNB2 had been one of the top hit regions in a GWAS of bipolar disorder I in a Han Chinese population CACNB2, together with the calcium channel alpha(2)/delta subunit, affects the kinetics and expression of Ca(V)1.2 (encoded by CACNA1C) CACNA1C is a well-established susceptibility gene for bipolar disorder, SCZ, and major depressive disorder CACNB2 resulted in the detection of the intronic SNPs rs12257556 and rs10764566, and these were eQTLs for ARL5B. The gene ARL5B encodes a trans-Golgi network localized small G protein that has been described as a key regulator of retrograde membrane transport ARL5B expression may be involved in the dysregulation of axonal transport. Interestingly, a previous study found that the transcript of one of the most widely studied susceptibility genes for SCZ, DISC1, was an interacting molecule for a motor protein of axonal transport CACNB2 locus suggested an interplay with our second gene of interest, i.e. CTCF. Such a connection is also suggested with RAD21. A substantial body of literature describes an interaction between RAD21 and CTCF, particularly in neurons CACNB2 and RAD21/CTCF, moderate evidence is available from several protein-protein interaction databases (data not shown) for an interplay between CTCF, RAD21, and ARL5B.In the gene-based analysis, CTCF encodes a transcriptional regulator protein with 11 conserved zinc finger domains, and is an important modulator of conformational changes in chromatin ctcf gene in mice demonstrated that CTCF was a key regulator of neuronal differentiation, and was essential for neuronal diversity and functional neural networks CTCF, may alter synaptic connectivity and thus contribute to the etiology of SCZ. Intriguingly, evidence from the majority of CTCF SNPs (both genotyped and proxies) suggested that the variants influence RLTPR expression . The resulting protein has a RGD (Arginine-Glycine-Aspartic acid) motif FOXP2, which was among the top genes in eight of the 14 most implicated pathways. FOXP2 (forkhead-box P2) is a transcription factor with an essential role in the development of speech and language regions in the brain. The fact that SCZ patients often show language impairments such as reading difficulties FOXP2 a plausible SCZ candidate gene. Interestingly, a previous study reported an association between genetic variation in FOXP2 and SCZ in a Han Chinese population FOXP2 as an inhibitor of the promoter activity and protein expression of DISC1. The present study supports the hypothesis that FOXP2 plays an important role in SCZ on the level of the transcriptional regulation of target genes.Another top hit gene in the present study was AKT3. Besides being detected via the Global Test, this gene was the most significantly associated gene in the FORGE analysis of the PGC data. AKT3 is a serin/threonine protein kinase, and is a member of the AKT family. It is involved in many biological processes, including apoptosis and cellular proliferation AKT3 contributes to hemimegalencephaly The association with the apoptosis pathway was driven predominantly by a SNP which mapped to CTCF and CACNB2 (and possibly ARL5B) were identified as SCZ candidate genes.In conclusion, the present study demonstrated that use of information from databases focusing on cell-regulatory networks together with information from traditional pathway database resources can facilitate the identification of susceptibility factors for the complex neuropsychiatric disease SCZ. Through the application of a well-designed hierarchical framework, our study highlighted the importance of calcium channel signaling, cell adhesion, and the modulation of transcriptional regulation implicated in neuronal diversity, neurite growth, and synapse formation in the etiology of SCZ. In particular, Each participant provided written informed consent prior to inclusion and all aspects of the study complied with the Declaration of Helsinki. The study was approved by the ethics committees of all study centers. For the German samples, this comprised the Ethics Committee of the Rheinische Friedrich-Wilhelms-University Medical School in Bonn, Ethics Committee “Medizinische Ethik-Kommission II” of the University of Heidelberg, the Ethics Committee of the Friedrich-Schiller-University Medical School in Jena, and the Ethics Committee of the Ludwig-Maximilians-University Munich. Samples obtained through dbGaP were collected using institutional review board-approved protocols in three studies, i.e. Schizophrenia Genetics Initiative (SGI), Molecular Genetics of Schizophrenia Part 1 (MGS1), and MGS2.Participants from four datasets were included Table 1.http://pngu.mgh.harvard.edu/purcell/plink/, version 1.07). A VIF of 100 was used. The window size was set at 50 SNPs, and was shifted by 5 SNPs at each step. An LD-based pruned set of SNPs (Text S1 (section “SNP independence and LD-based SNP pruning”) and in Table S7.To accommodate the Global Test's assumption of independence between variables, the SNP set was reduced according to a variance inflation factor (VIF) and using a sliding window approach, as implemented in PLINK  of SNPs Table 1 https://pgc.unc.edu/ResultFiles/pgc.scz.2012-04.zip) were used.For the gene-based analysis, PGC data (Text S1).SNPs were annotated with information from dbSNP Build 127. The “seq-gene” file containing information for annotating the SNP rs numbers to ENTREZ gene IDs was downloaded from the NCBI ftp website (BUILD 36.3). SNPs were assigned to a gene if the SNP was located within the genomic sequence or within 20 kb of the 5′ and 3′ ends of the first and last exons in order to account for important regulatory regions http://www.broadinstitute.org/gsea/msigdb). This included the pathways from BioCarta (217 pathways), Chemical and Genomic Perturbations , Reactome (775 pathways), MicroRNA Targets (176 gene-sets), and Transcription Factor Targets (456 gene-sets). Information concerning GO terms Text S1 (section “Choice of pathways and gene-sets”) and in Table S2 and Figure S1.Selected gene-set collections were accessed from the Molecular Signatures Database  globaltest, version 5.12.0; Text S1, section “Subject vs SNP label permutations”).For the pathway-based analysis, the Global Test At the discovery stage of the analysis, less conservative correction for multiple testing was applied in order to prioritize the identification of associated pathways. This was a legitimate approach, since any false positives would be controlled for in the replication analysis. Multiplicity correction was applied for each individual collection of pathways/gene-sets. For pathways/gene-sets retrieved from the KEGG, Reactome, and MSigDB gene-set collections, the pathway scores were corrected for multiple testing using the Benjamini-Hochberg method covariates function implemented in the R package globaltestTo estimate the contributions of individual SNPs to a pathway- or a gene association, the component global test was performed using the Only pathways that were significantly associated with SCZ in the discovery set were followed-up . The webtool SNAP The third step . The corresponding pathway name in the x-axis is a pathway with the highest overlap (self-overlap is excluded).(TIF)Click here for additional data file.Figure S2Hierarchical clustering of replicated pathways. The data are the counts of overlapping implicated single nucleotide polymorphisms, as detected using the Global Test in the BOMA-UTR dataset.(TIF)Click here for additional data file.Figure S3Comparison of the p-values obtained from the single nucleotide polymorphism-label permutation and subject-sampling test for all gene-sets.(TIF)Click here for additional data file.Table S1A) BOMA-UTR datasets for top 27 schizophrenia associated pathways identified by the GlobalTest performed to account for gender differences, linkage disequilibrium-structure, and gene-set size, (B) for the independent datasets  for the top 27 schizophrenia associated pathways, (C) for BOMA-UTR dataset for top 14 replicated schizophrenia associated pathways identified by various analysis methods.Comparisons of FDRs (BH) and P-values (P) for ((DOC)Click here for additional data file.Table S2Comparison of redundancies in the subsets of the 6 pathway databases/gene-set collections.(DOC)Click here for additional data file.Table S3A) Genes overlapping between the 14 replicated pathways in the BOMA-UTR dataset and (B) the GAIN-MGS dataset. (C) Single nucleotide polymorphisms overlapping between the 14 replicated pathways in the BOMA-UTR dataset and (D) the GAIN-MGS dataset.((DOC)Click here for additional data file.Table S4List of schizophrenia (SCZ) associated genes, their p-values , and membership in the SCZ associated pathways discovered and replicated in the present study. Pathways in bold also showed an overall association using one of the other three methods  applied in the present study.(DOC)Click here for additional data file.Table S5Potential functional consequences of CTCF associated SNPs.(XLS)Click here for additional data file.Table S6Potential functional consequences of CACNB2 associated SNPs.(XLS)Click here for additional data file.Table S7The Global Test results for the discovered gene-sets remained significant when the test was repeated with varying degrees of multicollinearity in the data.(DOC)Click here for additional data file.Text S1Description of supplementary results and methods.(DOC)Click here for additional data file.


WNT signaling is a complex process comprising multiple pathways: the canonical β-catenin-dependent pathway and several alternative non-canonical pathways that act in a β-catenin-independent manner. Representing these intricate signaling mechanisms through bioinformatic approaches is challenging. Nevertheless, a simplified but reliable bioinformatic WNT pathway model is needed, which can be further utilized to decipher specific WNT activation states within e.g. high-throughput data.In order to build such a model, we collected, parsed, and curated available WNT signaling knowledge from different pathway databases. The data were assembled to construct computationally suitable models of different WNT signaling cascades in the form of directed signaling graphs. This resulted in four networks representing canonical WNT signaling, non-canonical WNT signaling, the inhibition of canonical WNT signaling and the regulation of WNT signaling pathways, respectively. Furthermore, these networks were integrated with microarray and RNA sequencing data to gain deeper insight into the underlying biology of gene expression differences between MCF-7 and MDA-MB-231 breast cancer cell lines, representing weakly and highly invasive breast carcinomas, respectively. Differential genes up-regulated in the MDA-MB-231 compared to the MCF-7 cell line were found to display enrichment in the gene set originating from the non-canonical network. Moreover, we identified and validated differentially regulated modules representing canonical and non-canonical WNT pathway components specific for the aggressive basal-like breast cancer subtype.In conclusion, we demonstrated that these newly constructed WNT networks reliably reflect distinct WNT signaling processes. Using transcriptomic data, we shaped these networks into comprehensive modules of the genes implicated in the aggressive basal-like breast cancer subtype and demonstrated that non-canonical WNT signaling is important in this context. The topology of these networks can be further refined in the future by integration with complementary data such as protein-protein interactions, in order to gain greater insight into signaling processes. The WNT signaling pathway plays an important role not only in embryonic development processes but also in adult tissue homeostasis and repair after injury. Furthermore, its aberrant regulation is involved in a range of human pathologies such as cancer and degenerative diseases . The widCurrently, there are a number of WNT signaling pathways in public databases, such as BioCarta , ReactomIn brief, the canonical WNT pathway is defined by the nuclear translocation of β-catenin, in which it functions as a co-activator of transcription. In the absence of WNT signaling, β-catenin is targeted by a destruction complex composed of AXIN, APC, and GSK3β leading to its degradation by the proteasome. Binding of a WNT ligand to a frizzled (Fz) receptor and its co-receptors LRP5/6 activates intracellular dishevelled (DVL), leading to inactivation of the β-catenin destruction complex. This in turn leads to the stabilization of β-catenin, its relocation into the nucleus, and triggers the expression of target genes facilitated by TCF/LEF1 transcription factors ,11. This2+ signaling, representing the non-canonical WNT pathway. On the one hand this pathway can result in the activation of small GTPases such as Rho, Rac, and Cdc42, which regulate cytoskeleton rearrangement and PCP. On the other hand, it can also regulate transcription by activating the transcription factors NFAT or AP-1. Generally speaking, the non-canonical WNT pathway is linked to changes in cell motility, cytoskeletal reorganization processes, and invasiveness [However, within the WNT signaling pathway, several other β-catenin-independent mechanisms exist that are generally defined by a lack of β-catenin-mediated transcription. Broadly, these cascades can be grouped into planar cell polarity (PCP) and WNT/Casiveness .While the separation between these β-catenin-dependent and -independent pathways has proven valuable in understanding the signaling output, there is a high degree of interaction between these different signaling components on several levels of the cascades: The WNT protein family in humans comprises 19 members, all of which appear to be neither intrinsically canonical nor non-canonical. It is rather the multitude of possible combinations between WNT ligands and the respective Fz receptors and non-Fz (co)-receptors such as ROR1, ROR2, and RYK that channel WNT signals into one of the WNT sub-pathways . ApproprThis unclear distinction of WNT signals and their consequences are illustrated by the example of breast cancer. The inappropriate activity of both canonical and non-canonical WNT pathways has been implicated in breast cancer initiation as well as progression . Breast The particular WNT pathway involved in this enrichment has so far eluded detection. On the one hand, there is evidence pointing to the activation of β-catenin-dependent WNT signaling in basal-like breast cancer primaries ,20. On tPhenotypic differences such as invasiveness are often attributed to differences in the transcriptome between the investigated populations. By comparing their expression profiles differentially expressed genes (DEGs) are identified, which can result in fairly long lists. To reduce the complexity of these lists of DEGs and unravel the underlying biological processes, gene-set enrichment or overrepresentation analysis is a widely utilized bioinformatic approach, which allows the identification of the most affected pathways ,25. AnotPrior knowledge representing the pathway is required in order to test for enrichment of differential genes in a certain pathway or to integrate expression data into a pathway network . SeveralThus the aims of this study can be considered as four-fold: 1) We seek to collect available WNT signaling data across different pathway databases in the computer-readable BioPAX format to allow semi-automatized processing. 2) We aim to assemble this knowledge into directed graphs representing signaling networks of distinct WNT sub-pathways. 3) To demonstrate that these newly constructed WNT networks reliably reflect distinct WNT signaling processes, we evaluate them in an application example in the context of WNT regulation in breast cancer. Based on our previous work  we generIn order to assemble public pathway knowledge into network models of WNT signaling pathways three steps were taken: 1) Pathway data were gathered across several databases and parsed. 2) Gene IDs were mapped on pathway graphs. 3) The graphs were sorted and merged into four networks.rBiopaxParser R-package [Five pathway databases were used to collect WNT signaling data: BioCarta , Reactom-package . Pathway-package  Fig 1A)rBiopaxPaThe pathway databases use different identifications to annotate pathway molecules. In most cases Entrez Gene or UniProt IDs were available as reference annotations for a node and these were converted into HUGO Gene Nomenclature Committee (HGNC) gene symbols. However, whenever no official ID was available, we chose the instance name property defined within the BioPAX model to represent a node label. Pathway entities that could not be mapped into gene expression data, such as small molecules, DNA, RNA, or nested pathways, were filtered out, while connectivity of the graphs was preserved by introducing new edges connecting the genes that were thus indirectly connected. Further, if multiple gene symbols were mapped onto a single node, such a node was split into multiple nodes and each one was assigned with a single gene symbol. Vice versa, if several nodes were annotated with the same gene symbol, these nodes were merged into one.Wnt signaling pathway we exported from the KEGG database contained canonical as well as non-canonical sub-pathways and therefore the corresponding connected components of the pathway graph were sorted into canonical WNT signaling and non-canonical WNT signaling groups, respectively  data repository . Three MThe human breast cancer cell line MCF-7 was purchased from the German Collection of Microorganisms and Cell Cultures  and the MDA-MB-231 breast cancer cell war purchased from the American Type Culture Collection . Both cell lines were maintained in RPMI-1640 medium  supplemented with 10% heat-inactivated fetal calf serum . For gene expression studies, cells were seeded on extracellular matrix -coated tissue culture wells. Total RNA was isolated using TRIZOL reagent according to the manufacturer’s instructions . RNA integrity for each sample was confirmed with the Agilent 2100 Bioanalyzer .Library preparation for RNA-Seq was performed using the TruSeq RNA Sample Preparation Kit  starting from 1000 ng of total RNA according to Illumina's instructions. Accurate quantitation of cDNA libraries was performed by using the QuantiFluor™ dsDNA System (Promega). The size range of final cDNA libraries was determined applying the DNA 1000 chip on the Bioanalyzer 2100 from Agilent (280 bp). The purified DNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced on the HiSeq2000 following the manufacturer's protocols. Illumina Casava 1.8.2 software used for base-calling.edgeR R-package [edgeR. Multiple testing correction was done using the Bonferroni method for the family wise error rate (FWER) [Quality checking of RNA-Seq data was done via FastQC . Alignment to the human transcriptome based on the ENSEMBL genome (version GRCh38 release 78) was performed using the STAR tool version 2.4.0 . Lowly e-package . Differee (FWER) . RNA-SeqGene sets corresponding to the four WNT networks were created as lists of node labels represented by HUGO gene symbols. Furthermore, additional gene subsets were produced as: 1) the difference of canonical and non-canonical sets, containing only unique canonical genes (can. \ non-can.), 2) the difference of non-canonical and canonical sets, containing genes belonging exclusively to the non-canonical gene set (non-can. \ can.), and 3) the intersection of these two sets (can. ∩non-can.). 4) The last gene set was created as a union of canonical and non-canonical sets (can. ∪non-can.).Fisher's exact test was performed in order to test for over-representation. When testing for enrichment of a gene set in a list of differential genes a ranking based analysis was performed using Wilcoxon rank-sum test .tkplot R-function from the igraph R-package. For visualization purposes the range for color coding of fold-changes was limited to ±3 and ±4 for the modules based on microarray expression data and on RNA-Seq expression data, respectively.In order to identify differentially regulated WNT modules the up- and down-regulated DEGs were mapped onto the WNT network nodes. In the next step, nodes induced by DEGs were used as terminal nodes for the Steiner tree algorithm as implemented in the SteinerNet R-package ,42. In tcalcGraphOverlap R-function from the rBiopaxParser R-package was utilized and the modules based on microarray data were used as reference graphs.The differentially regulated modules of WNT networks originating from the public microarray expression data and the newly generated RNA-Seq data were investigated for graph overlaps. Therefore, the Several publicly accessible databases provide WNT pathways to differing extents. We collected 26 pathways linked to WNT signaling and sorted them into four conceptual groups . We defiWithin each group, the pathway graphs were merged into a singular signaling network resulting in four distinct networks  Figs: cabeta-catenin independent WNT signaling pathway graph.The size of the non-canonical network  exceeded that of the canonical network , even though the number of scientific publications seems to be imbalanced in favor of canonical WNT signaling knowledge . The greIn order to create a computationally suitable signaling network model in an automatized and reproducible way, the used pathways were limited to the databases using the BioPAX model. We focus on defining a graphical model of the WNT signaling pathways that can be used in network-integration analysis of static expression data. Our model does not contain any kinetic or mechanistic information. Alternative modeling approaches such as kinetic WNT models ,46 basedOwing to their considerable size, systematic manual inspection of the networks was challenging. However, before we began integrating the new networks with any experimental data we wanted to assess the quality of these networks. To evaluate their biological soundness, we took two approaches: First, we examined the interconnections of well-established WNT pathway members within our networks. Second, we identified key nodes in the networks which represent central signaling genes.In order to ascertain the biological relevance of the newly constructed networks, we were interested in surveying how well-known WNT pathway genes are represented therein. We collected an unbiased list of 121 genes of different WNT sub-pathways reviewed in several publications ,15,47,482+ pathway , are not included. Therefore, we do not expect the networks to be the complete representation of cell signaling processes. For a more complex view on the signaling pathways these networks could be further combined with complementary information, such as PPI data.In conclusion, this subnetwork revealed the relevant topology of the core members of WNT signaling. Moreover, its densely clustered communities and their interconnections reflect established biological models of WNT signaling. Although we reused knowledge from the literature within the subnetwork construction, our approach allowed us to acquire relevant insight into the WNT networks in general.To further examine the features of the four newly constructed WNT networks we identified their key nodes  between the cell lines, from which 1037 genes were up-regulated and 732 genes were down-regulated  in MDA-MOur next step was to integrate the differential genes between the MDA-MB-231 and MCF-7 cell lines with the graph representations of canonical and non-canonical signaling pathways.Differentially regulated modules, containing nodes of DEGs and Steiner nodes for graph connectivity, were extracted from canonical and non-canonical networks. In these modules, the DEGs are placed into a context of their signaling interaction partners representing aggressive breast-cancer-specific WNT pathway networks.As expected from the sizes of the original WNT networks and the results of the enrichment analysis, the non-canonical module was greater in size and more richly interconnected than that of the canonical module. The non-canonical module comprised 267 edges and 82 nodes, 68 of which were induced by DEGs and 14 were connecting Steiner nodes . The can2+ signaling [Both the canonical and non-canonical WNT modules demonstrate the importance of Steiner nodes to maintain connectivity of the network. Either these genes are not differentially expressed under the different conditions or are not measured within the microarray experiment at all. Key cancer drivers are often not detected through the analysis of differential expression, but they play central role in networks by interconnecting many genes, which are expression-responsive . In the ignaling  towards ignaling . While tThus, the identified WNT modules highlight intriguing events as well as several genes, for which it might prove interesting to test for their biological relevance within the aggressive basal-like breast cancer subtype.Although we are aware of the absence of some connections in the WNT networks, it is not possible to infer any new edges when adopting this integration approach. However, there are other bioinformatic methods, e.g. nested effect models  or BooleIn order to confirm the findings of our integrative analysis we generated an independent dataset by performing transcriptome sequencing of MCF-7 and MDA-MB-231 breast cancer cell lines. Differential analysis of the RNA-Seq data yielded 6638 DEGs between the cell lines , from whWe further performed network integration using the RNA-Seq data in the same manner as previously in the microarray application–including enrichment analysis and the identification of differentially regulated modules.For the purpose of enrichment analysis the Fisher's exact test was not the most suitable in a setting with such a high number of differential genes. Therefore, we preferred the ranking-based enrichment approach utilizing the Wilcoxon rank-sum test. The non-canonical WNT gene set was significantly enriched (p = 0.0198) while the canonical WNT set did not show enrichment (p = 0.3302) in MDA-MB-231 cells. This falls in line with our formulated hypothesis about non-canonical WNT signaling having a predominant role over the canonical WNT pathway in the more aggressive basal-like breast cancer cells In contrast to the microarray expression data, no split of the gene sets into unique subsets was required to recognize this distinction.validation modules, while their counterparts–non-canonical and canonical WNT modules identified based on microarray data–are referred to as original modules.Next, the differentially regulated modules of non-canonical and canonical networks were revealed by integration of the WNT networks with the RNA-Seq expression data . We refeThe non-canonical validation module  by evaluating the graph overlaps on node and edge levels while using the original modules as reference, 2) by assessing the correlation of folds-changes between the overlap genes of the corresponding modules and 3) by inspecting the consistency of Steiner node statuses.Non-canonical modules showed a node overlap of 75.6% and an edge overlap of 59.5% and a correlation of 0.81  for the non-canonical gene fold-changes. Canonical modules exhibited overlaps of 78.4% at the node-level and 70.8% at the edge-level. Their correlation was 0.81 .Prominent agreement was further found when focusing on the connecting Steiner nodes of the original modules. In case of the 14 non-canonical Steiner nodes, 10 of them were found in the corresponding validation module: 8 as Steiner nodes  and 2 were detected as significantly differentially expressed genes (FURIN and PPARGC1A). From the 10 Steiner nodes of the original canonical module, 5 were confirmed as Steiner nodes in the validation module  and 4 were identified as DEGs .Within the validated Steiner node genes, several have already been investigated in the context of aggressive breast cancer cells or tumors. For instance, NFATC2 was reported to promote breast cancer cell migration and to induce invasion . SimilarIn summary, the validation modules showed high level of topological as well as transcription concordance with the original differentially regulated WNT modules from the microarray analysis. Moreover, the validated nodes underline the importance of the Steiner nodes within the modules' topology and point to genes with a substantial role in invasiveness of breast cancer cells. The relevance of these nodes would have been neglected by a standard differential analysis without using the signaling network information.We constructed computationally suitable network models of WNT signaling cascades in a form of directed signaling graphs and demonstrated in the application example that these WNT networks reasonably reflect distinct WNT sub-pathways. We subsequently integrated the networks with microarray data to better understand the underlying biology of gene expression differences between different breast cancer cell lines. We identified differentially regulated modules representing canonical and non-canonical breast-cancer-specific WNT pathway components, validated these modules in an independent transcriptomic dataset, and highlighted relevant function of topologically-essential elements of the modules.Following our approach, the intricate WNT networks were shaped into a comprehensive graphical overview of the genes implicated in the aggressive basal-like breast cancer subtype. Utilizing these models as gene sets, we revealed that non-canonical WNT signaling is of importance in this system. Further enhancement of the network topology may be achieved by integration with complementary data types, such as PPIs, to gain even greater insight into pathway signaling processes.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 File(EPS)Click here for additional data file.S1 TableThe table contains pathway names, source databases and assigned groups for the network construction.(XLS)Click here for additional data file.S2 TableThe directed edges are weighted as 1 for activation and as -1 for inhibition interaction.(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 TableUp- and down- regulated significantly differentially expressed genes in the MDA-MB-231 cell line compared to the MCF-7 cell line.(XLS)Click here for additional data file.S5 TableSignificantly differentially expressed genes in the MDA-MB-231 cell line compared to the MCF-7 cell line.(XLS)Click here for additional data file.S6 Table(XLS)Click here for additional data file.


This was shown by using 14C-based fluxomics to determine the Rubisco in vivo carboxylation-to-oxygenation ratios.Photorespiration raises cellular CO 2-concentrating mechanism in C3–C4 intermediate plants is seen as a prerequisite for the evolution of C4 photosynthesis, but it is not known how efficient this mechanism is. Here, using in vivo Rubisco carboxylation-to-oxygenation ratios as a proxy to assess relative intraplastidial CO2 levels is suggested. Such ratios were determined for the C3–C4 intermediate species Flaveria pubescens compared with the closely related C3 plant F. cronquistii and the C4 plant F. trinervia. To this end, a model was developed to describe the major carbon fluxes and metabolite pools involved in photosynthetic–photorespiratory carbon metabolism and used quantitatively to evaluate the labelling kinetics during short-term 14CO2 incorporation. Our data suggest that the photorespiratory CO2 pump elevates the intraplastidial CO2 concentration about 3-fold in leaves of the C3–C4 intermediate species F. pubescens relative to the C3 species F. cronquistii.Formation of a photorespiration-based CO In C3 plants, CO2 enters metabolism directly via ribulose 1,5-bisphosphate (RubP) carboxylase/oxygenase (Rubisco). In the mesophyll of C4 plant leaves and in CAM (crassulacean acid metabolism) plants, CO2 is initially fixed by phosphoenolpyruvate carboxylase. The resulting four-carbon (C4) compounds are decarboxylated in the Rubisco-containing bundle-sheath of C4 plants  has received particular attention because it includes species with CO2 assimilation modes ranging from C3 via a broad range of C3–C4 intermediate species to C4  in leaves of C4 plants , these se sheath . This ine plants . Today, 2 pump could be. Here, 14CO2 incorporation studies designed to obtain an estimate of the in vivo rates of the two Rubisco-catalysed reactions in the C3–C4 species Flaveria pubescens relative to the control C3 species Flaveria cronquistii are reported. The ratio of these reactions, carboxylation versus oxygenation of RuBP, is co-determined by kinetic parameters of Rubisco and by the CO2/O2 concentration ratio , Flaveria pubescens Rydberg (C3–C4), and Flaveria trinervia (Spreng.) C. Mohr (C4) were grown in soil in a controlled environment chamber at 28/22 °C (day/night) and 250–300 µmol photons m–2 s–1 at a photoperiod of 16h. Fully expanded leaves excised from 40–60-d-old plants were fixed by thin wires in a frame positioned in a purpose-built fast-acting 14CO2 labelling device (12CO2 and 210 kPa O2 for 10–15min at about 1200 µmol photons m–2 s–1 and 25 °C to ensure maximum stomata opening and achievement of the steady-state rate of photosynthesis. Plants were then exposed to 14CO2 (2000 MBq mmol–1) for 0.6, 1.2, 2.4, 5, 15, 60, 120, and 360 s at the same concentrations of CO2 and O2, temperature and light as applied during pre-illumination. At the given time points, within 0.1 s, the leaf samples were automatically transferred into boiling 80% ethanol. 14CO2 incorporation was linear over the whole experiment. All experiments were performed in triplicate .2O each and cleared by centrifugation. The supernatants were combined, dried as above, and the metabolites re-dissolved in 1ml H2O. This final extract was used to determine total extractable radioactivity, radioactivity in amino acids , and other metabolites by using two-dimensional paper chromatography. Residual radioactivity in the fully extracted, dried, and triturated leaf samples was determined by using a non-aqueous scintillation cocktail. These analytical methods including the protocol used for starch analysis were described in more detail elsewhere  and day respiration (15 kPa O2) were distinguished by measurement under different O2 concentrations. Re-fixation ratios (D) of photorespiratory CO2 were calculated from 14CO2 evolution at the very high concentration of 3 kPa 12CO2, where re-fixation of 14CO2 evolved inside the cell is close to zero, relative to 14CO2 evolution at air levels of 12CO2.Rates of net and true photosynthesis, photorespiratory COd before , 2007. I14CO2 fed to leaves, the amounts of carbon incorporated at the selected time points were calculated and plotted against the duration of feeding with 14CO2. The amounts of carbon fixed in individual compounds were expressed in absolute (µmol C m–2) and relative  units. These experimental labelling curves contain the information about rates of all relevant carbon fluxes and corresponding metabolite pool sizes.From the radioactivity values for individual metabolites in combination with the specific radioactivity of the Flaveria photosynthetic–photorespiratory metabolism, the model shown in 2 incorporation into the reductive pentose phosphate cycle (RPPC) either directly with rate R1 or via the C4 cycle with rate R6. Total carbon flux through the photorespiratory cycle is denoted R2. R5 is the export rate of phosphorylated sugars into other pathways, for example, sucrose biosynthesis. R7 denotes the rate of carbon efflux from the RPPC to the C3 skeleton of C4 acids, while R8 describes the rate of accumulation of C4-acids. In order to simplify calculations, metabolites were grouped into four pools: (i) pool ‘SP’ with sugar phosphates plus 3-phosphoglycerate, (ii) pool ‘Gly’ with metabolites of the two-carbon branch of the photorespiratory cycle, (iii) pool ‘Ser’ with metabolites of the three-carbon branch of the photorespiratory cycle, and (iv) pool ‘C4’ with malate and aspartate. Each of these four pools comprises two metabolic sub-pools with different labelling kinetics, for example, photorespiratory pools with rapid turnover in peroxisomes and mitochondria  or less mobility in the cytosol and chloroplasts . At steady-state photosynthesis, these pools are in diffusional equilibrium with exchange rates R3 and R4, respectively. At the glycine-into-serine conversion step, one molecule of CO2 is released per serine molecule formed, corresponding to a glycine decarboxylation rate of R2/4. The resulting CO2 is re-fixed in the RPPC or the C4 cycle or escapes from the leaf. The extent of re-fixation is described by the re-fixation coefficient D, which was experimentally determined as described above.To extract this information on Ci and carbon fluxes Ri, the experimental values of the radioactivity of sugar phosphates, metabolites of the glycine and serine branches of the photorespiratory pathway, and of C4-acids were simultaneously fitted to these functions by multi-component non-linear regression analysis. These functions also consider the time-dependent dilution of the applied tracer CO2 by unlabelled photorespiratory CO2, which is important particularly at the start of tracer feeding under steady-state photosynthesis. A more detailed explanation of these functions is provided in the Supplementary data at JXB online.Formally, the metabolic model is described by the four analytical functions shown as equations 1–4, one for each major metabolite pool , F. pubescens (C3–C4 intermediate), and F. trinervia (C4). These species have previously been examined for their photosynthetic types  shown as equations 1–4.Building upon previous studies (P incorporated under steady-state conditions into each of the four major model components sugar phosphates plus 3-phosphoglycerate , the glycine branch  and the serine branch  of the photorespiratory pathway, and the C4 pathway . SS and SC are time-dependent functions that describe changes in the specific radioactivity of CO2 fixed in the RPPC and the C4 pathways, respectively. Functions P(SP), P(Gly), P(Ser), and P(C4) were simultaneously fitted to experimental data points collected over a time scale from 0.6 to 360 s during steady-state photosynthesis. Quantitative values for carbon fluxes Ri between the sub-pools directly involved in photosynthetic CO2 fixation and photorespiration, for example, from SP-I (pool size C1) via Gly-I (pool size C2) to Ser-I (pool size C3), were calculated by multi-component non-linear regression analysis.Essentially, these four functions describe the time dependence of the radioactivity 2 fixation by the C4 pathway in F. trinervia in combination with final refixation of CO2 released from C4 acids by the RPPC as well as the ‘glycine anomaly’ of the C3–C4 intermediate plant F. pubescens. As mentioned in the Introduction, the specific alterations to glycine metabolism of C3–C4 intermediate plants are due to a specific distribution of photorespiratory GDC activity , and export as a metabolically less mobile pool . Also, two metabolic pools with different labelling kinetics exist in both branches of the photorespiratory pathway. This is because one fraction each  is present in cellular compartments that do not directly contribute to photorespiratory reactions. These fractions show a lower turnover than the photorespiratory most active pools . At steady-state photosynthesis, the pools equilibrate pairwise with exchange rates R3 and R4. To consider such effects, and specifically calculate fluxes between metabolite pools directly involved in CO2 fixation and photorespiration, the model allows overlapping pools with different labelling kinetics to be separated by component analysis. F. pubescens. The example data display carbon incorporation into high- (Gly-I and Ser-I) and low-turnover (Gly-II and Ser-II) pools within the glycine and serine branches of the photorespiratory pathway. They also demonstrate the quantitative separation of the ‘active’ C4 carbon pool of C4 acids from label appearing in carbon atoms 1–3 and in C4 acids exported to the vacuole. Collectively, these data show that the chosen model is an adequate tool for the calculation of fluxes through the major routes of photosynthetic CO2 fixation from quantitative 14CO2 labelling data.Another apparent feature is the overlap of primary and secondary labelling kinetics, which is best seen with the Cn R6 in , seconda2 evolution. They allowed calculating the extent to which photorespiratory CO2 is re-fixed.The relevant fluxes are summarized in 2 can become incorporated into the RPPC either directly with rate R1 or indirectly via the C4 pathway with rate R6. The sums R1+R6 then represent total CO2 incorporation from external sources and show an increasing contribution by the C4 cycle, very low in F. cronquistii, low in F. pubescens, and, as expected, very high in F. trinervia. These total influx rates correspond reasonably well to directly measured rates for true photosynthesis PT, which provides a strong argument for the soundness of all other flux calculations. Higher values for PT (C3<C3–C4<C4) go together with increased rates of sucrose formation . Moreover, the photosynthetically active pools of C4 acids  via 57±19 (C3–C4) to 161±39 µmol C m–2 (C4). It is important to note that the increase of C4 cycle activity from F. cronquistii to F. pubescens  is only very small in comparison with the activity of the C4 cycle in F. trinervia (81.7% of PT). This suggests that CO2 accumulation occurs mainly by glycine-shuttling and less by C4 cycle activity in the bundle sheath of F. pubescens.COR2, is stoichiometrically related to the rate of RuBP oxygenation, R2/2. As a result of the operation of CO2-concentrating mechanisms in F. pubescens and in F. trinervia, photorespiration-related fluxes become distinctly lower from C3 towards C4 metabolism. To determine the true rates of RuBP carboxylation, in addition to the sum of R1 and R6, it was necessary to consider the refixation of CO2 generated from internal sources. In C3 and C3–C4 plants, photorespiration is the dominating internal source of CO2 during photosynthesis. R2 is stoichiometrically related to photorespiratory glycine decarboxylation as R2/4, because one molecule of CO2 is released per one molecule of serine formed from two glycine molecules. The extent to which refixation occurs must be separately determined. This was done by radiogasometric measurements , this assumption allows assessing the partitioning D between re-fixation and loss of CO2 from the leaf. The calculated total rates with which Rubisco fixes CO2 arriving by diffusion from the stomata (R1), from decarboxylation in the C4 cycle (R6), and from photorespiration (D*R2/4) were related to RuBP oxygenation rates (R2/2). The comparison shows that the resulting in vivo carboxylation-to-oxygenation ratio of Rubisco is more than three times higher in F. pubescens relative to F. cronquistii under the same experimental conditions.Carbon flux through the glycolate cycle, urements , 2007, w4Flaveria species has a somewhat lower affinity to CO2, but it is also known that Rubisco from C3 and C3–C4Flaveria species show more or less identical kinetics (3 plants is only slightly above air levels (F. cronquistii and F. pubescens but presumably also in the C4 species F. trinervia. Therefore, in a comparison of these species, measurement of in vivo carboxylation-to-oxygenation ratios allows the calculation of the relative CO2 concentration in chloroplasts. Considering the reported Km values of Rubisco for CO2, which are even somewhat higher than steady-state internal CO2 levels, our data suggest that the photorespiratory CO2 pump elevates the mean intraplastidial CO2 concentration during steady-state photosynthesis about 3-fold in leaves of the C3–C4 intermediate species F. pubescens relative to the C3 species F. cronquistii. This is considered to be a sound estimate because small contributions from C4 photosynthesis are balanced by the operation of a significant fraction of Rubisco at non-elevated CO2 levels in the mesophyll of F. pubescens.Rubisco from CJXB online.Supplementary data can be found at Supplementary data. An explanation of the labelling functions of the model shown in


Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways and relative enrichment is assessed, help investigators to propose biologically relevant hypotheses. The last generation of pathway analysis methods takes into account the topological structure of a pathway, which helps to increase both specificity and sensitivity of the findings. Simultaneously, the RNA-Seq technology is gaining popularity and becomes widely used for gene expression profiling. Unfortunately, majority of topological pathway analysis methods remains without implementation and if an implementation exists, it is limited in various factors.We developed a new R/Bioconductor package ToPASeq offering uniform interface to seven distinct topology-based pathway analysis methods, of which three we implemented de-novo and four were adjusted from existing implementations. Apart this, ToPASeq offers a set of tailored visualization functions and functions for importing and manipulating pathways and their topologies, facilitating the application of the methods on different species. The package can be used to compare the differential expression of pathways between two conditions on both gene expression microarray and RNA-Seq data. The package is written in R and is available from Bioconductor 3.2 using AGPL-3 license.ToPASeq is a novel package that offers seven distinct methods for topology-based pathway analysis, which are easily applicable on microarray as well as RNA-Seq data, both in human and other species. At the same time, it provides specific tools for visualization of the results.The online version of this article (doi:10.1186/s12859-015-0763-1) contains supplementary material, which is available to authorized users. High-throughput gene expression technologies (such as microarray or RNA-Seq) are used to estimate expression levels of thousands of genes in one experiment. Often the aim of such experiments is to find pathways and biological processes altered between two conditions, which helps investigators to propose biologically relevant hypotheses for further research. Achieving this aim implies integration of a priori known pathway information into the data analysis. Most often, a set of genes with similar biological function or participating in a regulatory process is employed as a set of entities in enrichment-based methods . This apmultivariable and univariable methods. For more detailed description of differences between multivariable vs univariable methods, we refer the reader to Additonal file Several types of methods for topology-based pathway analysis were proposed in the recent years (for review see ) - in alHere, we focus on methods that (i) aim to identify pathways affected between two conditions based on differential expression of genes in the pathway - the most frequent aim of high-throughput genomic data studies, (ii) use the a priori known pathway topologies and (iii) use the pathway topologies separately.The vast majority of existing topology pathway analysis methods were designed for continuous gene expression measures as obtained from microarray experiments. In order to apply them to discrete count data - a typical output from RNA-Seq experiment (number of reads mapped to a particular gene) - one must use a suitable transformation. Poisson or Negative binomial distribution are used as model distributions in differential expression analysis at gene-level for RNA-Seq data and a wide range of both transformation methods and statistical tests for this purpose exists. Performance of these methods is only recently being compared in extensive simulation studies –7.The published methods are only rarely implemented as a publicly available software tool or package, and sometimes the existing implementation is not available anymore (e.g TAPPA ). The exHere, we present ToPASeq  - a Bioconductor package that adjusts the set of methods available through graphite and extends them by addition of three more methods. The package offers their unified manipulation and provides tools for their easy application on RNA-Seq count data. In addition, it provides special functions for conversion of user-imported pathways into Pathway class and a set of tools for coercing graphs between different formats and manipulation and visualization of the results.In section ToPASeq was implemented using statistical programming language R and the package is available through the open-source Bioconductor project .In order to apply a topology-based pathway analysis method we need (i) gene expression measurements (a gene expression data matrix in which rows refer to genes and columns to samples), (ii) a vector with sample class labels and (iii) a list of pathways of interest together with their topologies in a specific format. The gene expression measurements and sample class information are usually available from the experiment.Pathway topologies are necessary for topology-based pathway analysis and can be created manually, or - even better - obtained from public databases or R packages, where they are typically stored in one of the standardized formats . These formats, however, need to be parsed (downloaded and converted to specific format) to be used within the methods’ particular implementations. Within R framework, multiple ways exist for pathway topology/graph representation. More detailed description of some of them in the context of biological pathways can be found in Additional file Our package requires the pathway topologies in format defined as S4 class PathwayList where individual pathways are of class Pathway, which allows combination of oriented and not-oriented edges as well as multiple edges between nodes. We have especially designed several transformation functions that convert the most common formats into Pathway.The users might be interested in manual editing of topology of the parsed pathways. We added group of methods such as (i) adding/removing of the nodes and edges, (ii) changing the type of interaction/directionality, (iii) merging two pathways into one, (iv) obtaining the induced subgraph. Additionally, the user may need to select only a subset of pathways based on their topological properties . These can be easily obtained with other set of available functions.Moreover, we especially designed a new function reduceGraph which merges the user defined named sets of nodes into a single node. The members of the sets must form either a gene family or a protein complex. The another function estimateCF estimates the maximal list of the sets of the nodes that can be merged. Finally, we provide a general function convertIdentifiersByVector which requires user specified information. For the detailed desctiption of the functionalities mentioned above we refer the reader to Additional file The package offers seven different methods covering various approaches in topological pathway analysis  work directly with normalized gene expression values, others  use the result of differential gene-expression analysis with or without application of significance thresholds to obtain the list of differentially expressed genes Fig. . With rep-values). Note, that PWEA method requires also so called Topology Influence Factors (TIFs), which need to be calculated from normalized gene expression data matrix.Each method is implemented as a single wrapper function which allows the user to call a method in a single command. The wrapper function offers: (i) normalization of count data; (ii) differential gene expression analysis and (iii) pathway analysis. The data input types were unified for all the methods. Expression data can be supplied both as matrix or as ExpressionSet. The functions’ outputs have uniform format defined as a new S3 class topResult with specified output of generic functions  and methods for accessing individual slots of the resulting object. The users can specify which method should be used for normalization or differential expression analysis of the RNA-Seq data, with respect to their own preferences. This data pre-processing step can be completely omitted and users can submit already normalized data or, if appropriate, the results of the differential expression analysis  knockdown HeLa cells . The RNA... download the KEGG pathways and apply all seven topology-based pathway methods:p-value less than 0.05 and the absolute log fold change above 2. Further, the gene identifiers in pathways are automatically converted to the EntrezGene identifiers and the non-oriented edges are oriented in both directions, when required.The arguments of all functions are as follows (from left to the right): a count matrix (or gene expression data matrix), a grouping vector, list of pathways with topologies and a type of the data). The TMM normalization and the limma-based differential gene-expression analysis are used by default. The pre-set thresholds for considering a gene significant are The results for an individual pathway can be visualized as shown in Fig. 1 in Additional file http://zlab.bu.edu/PWEA/download.php. The expression data have to be in the GSD format from Gene Expression Omnibus, where the probesets are named by both manufacturer IDs and the gene symbols. It is coupled with python script for retrieving and processing of KEGG.xml and.gene files. Beside the limitation to KEGG pathways and the need for manual downloading of non-human pathways or conversion to KGML format, it can be run only on UNIX-like systems. Recently, a standalone MATLAB-based implementation of PRS was published [The known previous implementations of the methods (if any) offered in ToPASeq are summarized in Table ublished . The apphttp://brainarray.mbni.med.umich.edu) or differential expression analysis; nor can it be used to analyse the RNA-Seq data.None of these tools allows for different method for normalization : Platform independent Programming language: R Other requirements: R version 3.2.1, CRAN and Bioconductor packages: graphite (>= 1.14), graph, gRbase License: AGPL-3 Any restrictions to use by non-academics: none Availability of supporting data: EBI ArrayExpress Experiment E-MTAB-1147: http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1147/, also in gageData packageProject name: ToPASeq Project home page: Additional file 1Supplementary material.pdf. The file contains additional details on the following: i) common principles of the multivariable and univariable topology-based methods; ii) the functions for pathway creation and manipulation (desciption as well as demostration); iii) comparison of ToPASeq with existing tools. (1013 Kb)


This encapsulation mechanism of lithiated/delithiated polysulphides was further confirmed by observations of shifted FTIR spectra for the C = N and C-N bonds, the XPS spectra for the Li-N bonds from nMOF-867, and a visualization method, demonstrating that nMOF-867 prevents lithium polysulphides from being dissolved in the electrolyte. Indeed, a cathode fabricated using nMOF-867 exhibited excellent capacity retention over a long cycle life of 500 discharge/charge cycles, with a capacity loss of approximately 0.027% per cycle from a discharge capacity of 788 mAh/g at a high current rate of 835 mA/g.Lithium polysulphides generated during discharge in the cathode of a lithium-sulphur redox cell are important, but their dissolution into the electrolyte from the cathode during each redox cycle leads to a shortened cycle life. Herein, we use  To confirm the self-encapsulation of polysulphides in the microcages, we used a combination of experimental characterizations including in situ spectroelectrochemical measurements, FTIR spectroscopy to identify C = N and C-N bonds, XPS spectroscopy to determine the existence of Li-N bond orbitals during discharge, and a visualization method. This is the first direct demonstration confirming the mechanism and interaction of lithium polysulphide with functional heteroatoms in the organic linkers of MOFs.In principle, the functional heteroatoms embedded in the organic linkers in metal-organic frameworks (MOFs)1718204) and H2BPYDC in N,N-dimethylformamide (DMF) in a 20 mL glass vial at room temperature. The sp2 nitrogen in BPYDC exists as a heteroatom replacing the = CH- group in the aromatic ring. Then, acetic acid and triethylamine (TEA) were added to the stock solution, which was sonicated for 20 min. The glass vial was placed into an oven at 85 °C for 12 hrs to grow the nanocrystalline MOFs. For nUiO-67, a H2BPDC organic linker was used in place of H2BPYDC in nMOF-867, and all of the other ingredients as well as the heating process were the same as those used for nMOF-867 (nMOF-867 was prepared by dissolving zirconium chloride (ZrClnMOF-867 . The pro2/g for nMOF-867 and 2256 m2/g for nUiO-67) provided sufficient space for retaining the sulphur. The octahedral morphologies of nMOF-867 and nUiO-67 that were ca. 500 nm in diameter were also determined through SEM measurements (The structures were characterized by powder X-ray diffraction (PXRD), nitrogen gas adsorption isotherm measurements, scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), energy dispersive X-ray spectroscopy (EDS) and thermal gravimetric analysis (TGA). The PXRD patterns for nMOF-867 and nUiO-67 contained sharp diffraction peaks at defined positions that were in agreement with simulated patterns24urements . Notably2/g, respectively , which resulted in the formation of a viscous slurry that was coated on aluminium foil and dried under vacuum at 60 °C for 24 hrs. Next, 2032-type cells were assembled with lithium foil as the counter electrode in an Ar-filled glove box. The electrolyte consisted of 1 M lithium bis(trifluoromethanesulphonyl)imide (LiTFSI) in N-methyl-N-butylpyrrolidinium bis(trifluoromethane sulphonyl)imide (PYR14TFSI)/1,2-dimethoxyethane/1,3-dioxolane (2:1:1 by volume) with LiNO3 (1 wt%) as an additive to help passivate the surface of the lithium anode and prevent the shuttle effect28We also explored the electrochemical performance of the Li-S redox system with nMOFs/S. First, the nMOFs/S was mixed with conductive super P and polyvinylidene fluoride (PVDF) binder in 8 to long-chain lithium polysulphides , and the plateau at approximately 2.1 V corresponded to the formation of short-chain lithium polysulphides, such as Li2S2 and Li2S30th to 8th cycles were similar to those of the 1st and 2nd cycles. This result indicated that the N atoms covalently bound to C atoms in the organic linkers of the nanocrystalline MOFs formed secondary interactions with lithium polysulphides. In addition, nMOF-867/S  of the MOF. Indeed, the results in 2 nitrogen in their aromatic rings over repeated discharge/charge cycles. Notably, excellent performance with a specific energy density of ~1700 Wh/g over 500 cycles at a high current rate of 835 mA/g was observed. Moreover, the capacity retention behaviours of nMOF-867 were maintained at high areal loadings of active materials up to 5 mg/cm2 3 and ZnO4(BPYDC)3), which were separately prepared with BPDC, which has no sp2 nitrogens, and BPYDC, which has sp2 nitrogens in its linker322 nitrogens  was much higher than for the IRMOF-10 with no sp2 nitrogens . These results confirmed that the sp2 nitrogen in the linker of the MOF structure enhanced the capacity retention over repeated discharge/charge cycles. Therefore, these results demonstrate that the simple introduction of heterogeneous atoms into the organic linkers in the microcages of MOFs can lead to excellent energy storage performance, thus opening a new route for providing an excellent solution for current energy storage issues.Additionally, we tested another set of Zn-based IRMOF-10s , which was synthesized by mixing stoichiometric amounts of Li2S and sulphur in tetraethylene glycol dimethyl ether (TEGDME)2S4) solution. Finally, the products were washed with methanol to remove excess polysulphide molecules, and this was followed by drying in a vacuum oven at 60 °C for 24 hrs.We further investigated the discharge/charge behaviour by using Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS), and these results provided detailed information regarding the chemical binding of sp2S4 are shown in −1), the asymmetric and symmetric stretching modes of the carboxylate , and the single bond stretching of C-O (1245 cm−1) and C-C (1163 cm−1)  shifted from 1537 to 1541 cm−1 for the C = N double bonds and 1364 to 1367 cm−1 for the C-N single bond because the polysulphides undergo chemical interactions with the sp2 nitrogen atoms, which shift its stretching frequency. Moreover, to obtain more detail on the chemical binding of the nitrogen sp2 orbitals during lithiation, we also determined the XPS spectra , Li-N (55.6 eV) and Li-S (54.6 eV). Figure 4d2 nitrogen orbitals interacted with the Li in Li2S4.The FTIR spectra of nMOF-867 and nMOF-867/Li63 cm−1)  indicate spectra  of pristof Li2S4  was fitt2S4 TEGDME solution for up to 240 min. Because Li2S4 in solution is yellow, we were able to visualize the migration of Li2S4 by following the colour change. The photos show the colour changes of the Li2S4 solution after mixing with both MOFs at regular intervals of 0 min, 120 min and 240 min. The results in 2S4 solution became transparent as Li2S4 was captured by MOF-867, and the nUiO-67 in the Li2S4 solution maintained its yellow colour even after 240 min  in the TEGDME solution. The initial colour of Li2S4 was yellow, and this colour was maintained after stirring for 240 min. These results provide clear evidence that the colour change was due to chemical interactions between sp2 nitrogens and lithium polysulphides and not to photooxidation.Additionally, we developed a visualization method by mixing nMOF-867 and nUiO-67 with a 0.1 M Li 240 min . These rnMOF-867 . HowevernMOF-867 . These reriments  to proviin situ spectroelectrochemical method using a UV-visible instrument  linked to a potentiostat  was developed. The cell system  was mixed with carbon black (Super P) and polyvinylidene fluoride binder in N-methyl-N-butylpyrrolidinium bis(trifluoromethane sulphonyl)imide (PYR14TFSI)/1,2-dimethoxyethane/1,3-dioxolane (2:1:1 by volume) with LiNO3 (1 wt%) was used as the electrolyte. The electrochemical measurements were carried out using 2032-tyte coin cells, and lithium foil was used as the counter/reference electrode. Polypropylene membranes (Celgard Inc.) were used as separators. Galvanostatic discharge/charge tests were performed by cycling between 1.7 and 2.8 V at 167 mA/g and 835 mA/g using an electrochemical redox cycler . Moreover, the capacity retention behaviours of nMOF-867 were investigated by varying the loading amount of the active materials from 3.6 to 5 mg/cm2 (1 M lithium bis(trifluoromethanesulphonyl)imide (LiTFSI) in 5 mg/cm2 .How to cite this article: Park, J. H. et al. Encapsulation of redox polysulphides via chemical interaction with nitrogen atoms in the organic linkers of metal-organic framework nanocrystals. Sci. Rep. 6, 25555; doi: 10.1038/srep25555 (2016).


High-throughput OMICs experiments generate signals for millions of entities  in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access. With the expansion and accessibility of a wide range of experimental techniques to accurately identify and measure any known genomics feature ranging from proteins, transcripts, genes, microRNAs, copy number variations, or DNA methylation in a high-throughput manner, signals for thousands of entities are often generated for an individual OMICs experiment. In efforts to interpret these results in the context of perturbed cellular mechanisms, the entities are often scored and examined for enrichment in known pathways and processes.et al.1, Huanget al.2). Due to the high redundancy that exists between pathway databases coming from multiple functional annotations of gene products, pathway enrichment often results in a long list of potentially interesting pathways. To help analyze the set of differential pathways, we created the Enrichment Map app to display enrichment results as a network, where pathways are nodes in the network and edges represent known pathway cross-talk defined by the number of genes shared between the pair of pathways and where the network layout organizes the map into functional modules3.Pathway enrichment analysis helps to uncover general trends or themes present in the data, instead of focusing on one or a few favorite differential genes. Available tools are abundant, designed for varying data types and implemented using a range of different statistical tests: given a set of biological entities, these OMICs signals are then translated into a set of significant pathways and processes http://download.baderlab.org/EM_Genesets/), including standard sources, like MSigDB4. Although originally GMT files were specific to GSEA, with the expansion of R and Bioconductor it is now straightforward to loadGMT files into data structures in R using packages like GSA (http://statweb.stanford.edu/~tibs/ftp/GSA.pdf) and analyze your OMICs expression data with one of the many different gene set enrichment algorithms such as geneSetTest in the Limma package9, global test10, or Camera11. Visualizing the resulting enrichments is straightforward by exporting to our generic format which minimally consists of the geneset name, description and associated enrichment p-value. Through this mechanism, no matter what the dataset of interest is, gene, protein or metabolite expression, the resulting enrichment analysis can be displayed as an enrichment map.With the ongoing effort to populate gene annotation and pathway databases, it is difficult for standalone enrichment tools to keep databases up to date. For convenience, we compile gene set files or GMT files, a format created for the GSEA software, to describe all the genes contained in a specified gene set, monthly, from a comprehensive set of annotation and Pathway databases  and their connections the set of genes that two nodes have in common . Every Enrichment Map is associated with a set of files, parameters, and a number of datasets (currently limited to two) . DatasetEnrichment Map app was ported to Cytoscape 3 as a bundle app using Open Service Gateway initiative (OSGi) services provided through the extensive Cytoscape API (version 3.1). The look and feel of the app remains similar to the original implementation for Cytoscape 2 with user input interfaces and view panels including expression heatmap and legend being a direct port from the original source. Given the new framework, each panel implements the CytoPanelComponent and is a registered service associated with the Enrichment Map app. The main enrichment map input panel is registered only once a user opens the app. The remaining view panels are only registered once an enrichment map is created. Enrichment Map consists of one main taskFactory that given an Enrichment Map object populated with a set of input files will construct the appropriate task iterator. Depending on the files specified different parsing tasks can be added to the iterator. Additionally, multiple files of the same type can also be added to the queue with distinct instantiations of a parsing task (with different files specified on task creation). All parsed files populate fields contained in the Enrichment Map object which is then passed to and updated by each of the subsequent tasks .The BuildEnrichmentMapTaskFactory is used by both the user interface and command tool to construct an enrichment map. Command tool functionality for Enrichment Map requires the given task to define its variables as tunables. Tunables are user supplied information needed by the task. User interfaces can be automatically generated for such tasks based on the set of tunable definitions. When implementing the Enrichment Map tunable task it was our intention to replace our current user interface with the one automatically generated by the task. Given the varied data required from the user as well as the interactive nature of our current user interface the generated tunable interface although functional lacked features that our users are accustomed to. For instance, to specify the analysis type or similarity cutoff our interface has two sets of radio buttons where all the options are visible and only one is selectable. In the tunable interface the same choice can only be represented as a single selection list, a drop down list the user can choose one option from. Both representations are functional but we preferred the radio button implementation therefore, we decided to keep our original interface and add the tunable task solely for the command tool functionality.12 for mouse fibroblast cells. The experiment was designed to compare gene expression in fibroblast cells in the heart to those in the tail to highlight genes that are uniquely expressed in heart fibroblasts13 (GSE50531). Raw expression data was scored using the GEO2R tool available on the GEO website. These expression data were input to GSEA along with a recent compilation of mouse pathway gene sets  to calculate enrichments. GSEA output files were given tothe app with the cutoffs p-value < 0.005, q-value < 0.05 and overlap similarity coefficient > 0.3. The Enrichment Map generated had roughly the same number of enriched gene sets specific to heart as to tail with cardiac specific sets associated only with the heart phenotype 13. In Enrichment Map it is easy to find all gene sets that contain it by entering the term TBX20 into the search box , the visualization of clusters and networks can be improved.The Enrichment Map app allows users to translate large sets of enrichment results to a network where highly similar terms cluster together to better highlight overall trends and themes of the underlying data. The details behind the enrichment can be further investigated within the Enrichment Map app using the built-in expression viewer to see all the entities associated with a selected pathway.http://apps.cytoscape.org/apps/enrichmentmapSoftware available from:https://github.com/BaderLab/EnrichmentMapAppLatest source code:https://github.com/F1000Research/EnrichmentMapApp/releases/tag/V1.0Source code as at the time of publication:http://dx.doi.org/10.5281/zenodo.1054214Archived source code as at the time of publication:https://www.gnu.org/licenses/old-licenses/lgpl-2.1.htmlLicense: Lesser GNU Public License 2.1:http://baderlab.org/Software/EnrichmentMap#Tutorials_and_ExamplesTutorials This paper is an update on the Enrichment Map introduced in 2010 by the same authors. They have extended the methodology and made it available in the newest version of Cytoscape.Gene set enrichment methods of various forms are one of the most widely used first steps to gain a global picture of which pathways or other functional units are involved in some molecular phenotype. However, it is generally very hard to make sense of the results - mostly because lists of enriched gene sets can be very long and might be due to a small set of genes that appear in many of them.This is where the Enrichment Map comes in: By making the overlap between gene sets explicit it allows users to visually explore 'enrichment themes' (clusters of overlapping gene sets). Because it is simple and informative I believe this will become the standard way to present enrichment results.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors present an already highly used and very useful approach for making sense of highly redundant biological enrichment results that arise from the analysis of transcriptome and genomics datasets. The added additional utility of accessing the software by command line and using alternative input formats makes this a highly accessible plugin for Cytoscape, that will clearly be widely adopted by the Cytoscape community through its availability in Cytoscape 3.0.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Insects are one of the most successful animal groups on earth. Some insects, such as the silkworm and honeybee, are beneficial to humans, whereas others are notorious pests of crops. At present, the genomes of 38 insects have been sequenced and made publically available. In addition, the transcriptomes of dozens of insects have been sequenced. As gene data rapidly accumulate, constructing the pathway of molecular interactions becomes increasingly important for entomological research. Here, we developed an improved tool, iPathCons, for knowledge-based construction of pathways from the transcriptomes or the official gene sets of genomes. Considering the high evolution diversity in insects, iPathCons uses a voting system for Kyoto Encyclopedia of Genes and Genomes Orthology assignment. Both stand-alone software and a web server of iPathCons are provided. Using iPathCons, we constructed the pathways of molecular interactions of 52 insects, including 37 genome-sequenced and 15 transcriptome-sequenced ones. These pathways are available in the iPathDB, which provides searches, web server, data downloads, etc. This database will be highly useful for the insect research community.Database URL:http://ento.njau.edu.cn/ipath/ Bombyx mori) and honeybee (Apis mellifera) are beneficial to humans by producing valuable products and/or services . In contrast, other species damage crops by feeding on leaves or fruits, causing huge economic losses.Insects are one of the most successful animal groups on earth. They comprise more than a million species, representing about half of all known living organisms. Some insects, such as silkworm , including Aedes aegypti (v2.2), Anopheles gambiae (v3.8), Anopheles darlingi (v2.2), Anopheles stephensi (v2.2), Culex quinquefasciatus (v1.4), G. morsitans (v1.3), Lutzomyia longipalpis (v1.1), Pediculus humanus corporis (v1.3), Phlebotomus papatasi (v1.1), Rh. prolixus (v1.1), Acromyrmex echinatior (v3.8), Atta cephalotes (v1.2), Camponotus floridanus (v3.3), Harpegnathos saltator (v3.3), Linepithema humile (v1.2), Pogonomyrmex barbatus (v1.2), Solenopsis invicta (v2.2.3), A. mellifera (v3.2), Nasonia vitripennis (v1.2), Drosophila ananassae (v1.3), Drosophila erecta (v1.3), Drosophila grimshawi (v1.3), Drosophila melanogaster (v5.54), Drosophila mojavensis (v1.3), Drosophila persimilis (v1.3), Drosophila pseudoobscura (v3.1), Drosophila sechellia (v1.3), Drosophila simulans (v1.4), Drosophila virilis (v1.2), Drosophila willistoni (v1.3), Drosophila yakuba (v1.3), Danaus plexippus (v2.0), Heliconius melpomene (v1.1), B. mori (v2.0), T. castaneum (v3.0), Ac. pisum (v2.1b) and Ti. cristinae (v1.0).The genome sequences of 37 insects were downloaded from the NCBI  genome database or in species-specific databases, such as FlyBase , AphidBahttp://www.ncbi.nlm.nih.gov/sra/). The genomic data of these species are not available at present. They are A. cerana, Lucilia sericata, Rhagoletis pomonella, Aedes albopictus, Galleria mellonella, Chilo suppressalis, Spodoptera exigua, Manduca sexta, Melitaea cinxia, P. xylostella, Zygaena filipendulae, Dendroctonus ponderosae, Nilaparvata lugens, Oncopeltus fasciatus and Bemisia tabaci. The SRA accession numbers are given in Supplementary Table S2. The transcriptome of C. quinquefasciatus was also downloaded and used to estimate the precision and coverage of the iPathCons.We downloaded the RNA-seq raw data of 15 insects from Sequence Read Archive database  data of Ga. mellonella, Me. cinxia, and Z. filipendulae, we assembled the transcripts using the Cap3 software  is a format of the KEGG pathway maps, which can be used to draw KEGG pathways and to model gene and chemical networks. Given that KEGG requires a subscription to its FTP server, we downloaded the KGML files from their individual web download pages.The KEGG database provides the most commonly used resources for pathway analysis . KEGG coWe developed a pipeline, iPathCons, to construct insect pathways using either OGSs or transcriptomes .Figure We downloaded the KEGG genes of 21 genome-sequenced insects, the OGS of 37 genome-sequenced insects and the transcriptome data of 15 species. For the 21 KEGG-annotated insects with OGS data, we compared the sequences in KEGG gene data and OGS data sets, (i) if there is a length difference, we used the long transcript; (ii) we kept those genes even they appear in only one gene data set.E-value ≤ 10−5), which has been widely used in the KOBAS . The KO term that appeared at the highest frequency (the minimum cutoff is ≥ 2) was used as the final KO annotation.A similar procedure was used to deduce pathway from the transcriptomes of 15 insects. All 37 genome-sequenced insects, including 21 KEGG-annotated and 16 iPathCons-annotated ones, were used as the template for KO assignment. The protein sequences of each genome-sequenced insect were used as the local BLAST database, respectively. The transcriptome sequences were used to BLASTP against the local BLAST database . We also used the transcriptome data of C. quinquefasciatus for pathway construction and obtained a similar result.We used A. cerana cerana and Ga. mellonella were used to deduce pathways by both iPathCons and KAAS. The results indicated that similar number of KO terms and pathways were annotated in A. cerana cerana by two tools, whereas the iPathCons found 1675 KO terms and 255 pathways, much more than 1511 KO terms and 239 pathways annotated by the KAAS  annotating an insect transcriptome or gene sets for pathway construction; (ii) generating KGML files that can be opened by VANTED  and KEGGWe constructed an insect pathway database named as the iPathDB, which was developed on a Linux operating system . The Apache HTTP server was used to handle queries from web clients through PHP scripts to perform searches. The web pages were written using html, PHP, CSS and JavaScript. The architecture of iPathDB is presented in Users can search insect pathways using keywords for species, pathway ID and pathway name. When using species name as the search keyword, all pathways for that species will be presented. When using pathway ID or pathway name as the search keyword, the pathway will be given for all species in the database. Search results provide gene sequences, annotations and a pathway map.An online iPathCons server was provided. The KEGG- and iPathCons-annotated gene sets from different insect orders, including Diptera, Lepidoptera, Coleoptera, Hymenoptera, Phthiraptera and Hemiptera, are used as the template for constructing insect pathways. Users can select a template according to their requirements. When the queried sequences are less than 10, the results are displayed in the Webpage directly. If the queried sequences are more than 10, a URL link of the iPathCons results will be sent to the user via e-mail.ftp://ftp.insect-genome.com/pub/iPathDB/. On the download page, insect pathway information classified by species and the stand-alone version of iPathCons can be downloaded. A phylogenetic tree is provided to show evolutionary relationships. The insect pathway files can be directly downloaded by clicking the links in the tree. On the FTP server, insect pathway information is provided by species. The zipped archive contains a readme file, sequences in FASTA format, KEGG map links, a KGML file, a pathway summary and a pathway list. In total, iPathDB contains 11 581 pathways from 52 species and 387 478 annotated sequences , ecdysteroid 22-hydroxylase and CYP302A1 (Dib) were found in almost all insects . We compD. melanogaster  of China (2012AA101505), the National Science Foundation of China  and the Jiangsu Science Foundation for Distinguished Young Scholars (BK2012028). Funding for open access charge: BK2012028.This work was supported by the Conflict of interest. None declared.